In Honor of the 4th of July, A Few Model-Data Comparisons of Contiguous U.S. Surface Air Temperatures

This post is similar in format to In Honor of Secretary of State John Kerry’s Global Warming Publicity-Founded Visit to Greenland…  As you’ll see, like Greenland, the consensus of the climate models used by the IPCC show that the models do not simulate the surface temperatures for the contiguous United States over any timeframe from 1861 to present. 


We illustrated and discussed the wide ranges of modeled and observed absolute global surface temperatures in the November 2014 post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison. Not long after came a post at RealClimate of modeled absolute global surface temperatures, authored by Gavin Schmidt, the head of the Goddard Institute of Space Studies (GISS).  Gavin’s post is Absolute temperatures and relative anomalies.  (Please read it in its entirety.  I believe you’ll find it interesting.)  Of course, Gavin Schmidt was downplaying the need for climate models to simulate Earth’s absolute surface temperatures.

In this post about the surface temperatures of the contiguous United States, we’ll present a few examples of why climate modelers need to shift their focus from surface temperature anomalies to absolute surface temperatures.  Why?  In addition to heat waves and cold spells, near-surface air temperatures play roles in model simulations of snow cover, drought, growing seasons, surface evaporation that contributes to rainfall, etc.

In the past, we’ve compared models and data using time-series graphs of temperature anomalies, absolute temperatures and temperature trends, and we’ll continue to provide them in this post.  In this series, we’ve added a new model-data comparison graph:  annual cycles based on the most-recent recent multidecadal period. Don’t worry, that last part will become clearer later in the post.


We’re using the model-mean of the climate models stored in the CMIP5 (Coupled Model Intercomparison Project Phase 5) archive, with historic forcings through 2005 and RCP8.5 forcings thereafter.  (The individual climate model outputs and model mean are available through the KNMI Climate Explorer.) The CMIP5-archived models were used by the IPCC for their 5th Assessment Report. The RCP8.5 forcings are the worst-case future scenario.

We’re using the model-mean (the average of the climate model outputs) because the model-mean represents the consensus of the modeling groups for how surface temperatures should warm if they were warmed by the forcings that drive the models. See the post On the Use of the Multi-Model Mean for a further discussion of its use in model-data comparisons.

I’ve used the ocean-masking feature of the KNMI Climate Explorer and the coordinates of 24N-49N, 125W-66W to capture the modeled near-land surface air temperatures of the contiguous United States, roughly the same coordinates used by Berkeley Earth.

Near-surface air temperature observations for the contiguous U.S. are available from the Berkeley Earth website, specifically the contiguous United States data here. While the monthly data are presented in anomaly form (referenced to the period of 1951-1980), Berkeley Earth provides the monthly values of their climatology in absolute terms, which we then simply add to the anomalies of the respective months to determine the absolute monthly values.   Most of the graphs, however, are based on annual average values to reduce the volatility of the data.

The model mean of surface temperatures at the KNMI Climate Explorer starts in 1861 and the Berkeley Earth data end in August 2013, so the annual data in this post run from 1861 to 2012.


Figure 1 includes a time-series graph of the modeled and observed annual near-land surface air temperature anomalies for the contiguous U.S. from 1861 to 2012. Other than slightly underestimating the long-term warming trend, at first glance, the models appear to do a reasonable job of simulating the warming (and cooling) of the surfaces of the contiguous United States. But as we’ll see later in the post, the consensus of the models misses the multidecadal warming from the early 1910s through the early 1940s.

Figure 1

Figure 1

Keep in mind, Figure 1 is how climate modelers prefer to present their models, in anomaly form.

Figure 2 gives you an idea of why they prefer to present anomalies. It compares the modeled and observed temperatures on an absolute basis. Not only do the models miss the multidecadal variations in the surface temperatures of the contiguous United States, the consensus of the models is running too cold.  That of course would impact how well the models simulate temperature-related factors like snowfall, drought, crop yields and growing seasons, heat waves, cold spells, etc.

Figure 2

Figure 2


Climate is typically defined as the average conditions over a 30-year period.  The top graph in Figure 3 compares the modeled and observed average annual cycles of the contiguous U.S. surface temperatures for the most recent 30-year period (1983 to 2012). Over that period, data indicate that the average surface temperatures for the contiguous U.S. varied from about +0.0 deg C (+32 deg F) in January to roughly +24.0 deg C (+75 deg F) in July.  On the other hand, the consensus of the models show they are too cool by an average of about 1.4 deg C (2.5 deg F) over the course of a year.

Figure 3

Figure 3

You might be saying to yourself, it’s only a model-data difference of -1.4 deg C, while the annual cycle in surface temperatures for the contiguous U.S. is about 24 deg C.  But let’s include the annual cycle of the observations for the first 30-year period, 1861-1890. See the light-blue curve in the bottom graph in Figure 3. The change in observed temperature from the 30-year period of 1861-1890 to the 30-year period of 1983-2012 is roughly 1.0 deg C, while the model-data difference for the period of 1983-2012 is greater than that at about 1.4 deg C.


Let’s add insult to injury. For the top graph in Figure 4, I’ve smoothed the data and model outputs in absolute form with 30-year running-mean filters, centered on the 15th year. Again, we’re presenting 30-year averages because climate is typically defined as 30 years of data.  This will help confirm what was presented in the bottom graph of Figure 3.

The models obviously fail to properly simulate the observed surface temperatures for the contiguous United States. In fact, the modeled surface temperatures are so cool for the most recent modeled 30-year temperature-based climate that they are even below the observed surface temperatures for the period of 1861 to 1890. That is, the models are simulating surface temperatures for the contiguous U.S. over the last 30-year period that have not existed during the modeled period.

Figure 4

Figure 4

For the bottom graph in Figure 4, I’ve extended the model outputs out into the future, to determine when the models finally simulate the temperature-based climate for the most-recent 30-year period. The horizontal line is that average data-based temperature for the period of 1983-2012.  Clearly, the future models are out of sync with reality by more than 3 decades.

Keep the failings shown in Figure 4 in mind the next time an alarmist claims some temperature-related variable in the contiguous U.S. is “just as predicted by climate models”.   Nonsense, utter nonsense.


The top graph in Figure 5 shows the modeled and observed 30-year trends (warming and cooling rates) of the surface air temperatures for the contiguous U.S. If trend graphs are new to you, I’ll explain. First, note the units of the y-axis.  They’re deg C/decade, not simply deg C.  The last data points show the 30-year observed and modeled warming rates from 1983 to 2012, and it’s shown at 2012 (thus the use of the word trailing in the title block). The data points immediately before it at 2011 show the trends from 1982 to 2011. Those 30-year trends continue back in time until the first data point at 1890, which captures the observed and modeled cooling rates from 1861 to 1890 (slight cooling for the data, noticeable cooling for the models). And just in case you’re having trouble visualizing what’s being shown, I’ve highlighted the end points of two 30-year periods and shown the corresponding modeled and observed trends on a time-series graph of temperature anomalies in the bottom cell of Figure 5.

Figure 5

Figure 5

A few things stand out in the top graph of Figure 5. First, the observed 30-year warming rates ending in the late-1930s, early-1940s are comparable to the most recent observed 30-year trends. In other words, there’s nothing unusual about the most recent 30-year warming rates of the surface air temperatures for the contiguous U.S.  Nothing unusual at all.

Second, notice the disparity in the warming rates of the models and data for the 30-year period ending in 1941. According to the consensus of the models, the near-surface air of the contiguous United States should only have warmed at a rate of about 0.12 deg C/decade over that 30-year period…if the warming there was dictated by the forcings that drive the models.  But the data indicate the contiguous U.S. surface air warmed at a rate that was almost 3.5 deg C/decade during the 30-year period ending in 1941…almost 3-times higher than the consensus of the models.  That additional 30-year warming observed in the contiguous United States, above and beyond that shown by the consensus of the models, logically had to come from somewhere.  If it wasn’t due to the forcings that drive the models, then it had to have resulted from natural variability.

Third thing to note about Figure 5:  As noted earlier, the observed warming rates for the 30-year periods ending in 2012 and 1941 are comparable.  But the consensus of the models show, if the warming of the near-surface air of the contiguous United States was dictated by the forcings that drive the models, the warming rate for the 30-year period ending in 2012 should have been noticeably higher than what was observed.  In other words, the data show a noticeably lower warming rate than the models for the most-recent 30-year period.

Fourth: The fact that the models better simulate the warming rates observed during the later warming period is of no value.  The model consensus and data indicate that the surface temperatures of the contiguous United States can warm naturally at rates that are more than 2.5 times higher than shown by the consensus of the models.  This suggests that the model-based predictions of future surface warming for the contiguous U.S. are way too high.


Climate science is a model-based science, inasmuch as climate models are used by the climate science community to speculate about the contributions of manmade greenhouse gases to global warming and climate change and to soothsay about how Earth’s climate might be different in the future.

The climate models used by the Intergovernmental Panel on Climate Change) IPCC cannot properly simulate the surface air temperatures of the contiguous United States over any timeframe from 1861 to present.  Basically, they have no value as tools for use in determining how surface temperatures have impacted temperature-related metrics (snowfall, drought, growing periods, heat waves, cold spells, etc.) or how they may be impacting them presently and may impact them in the future.

As noted a few times in On Global Warming and the Illusion of Control – Part 1, climate models are presently not fit for the purposes for which they were intended.


This is the third post of a series in which we’ve included model-data comparisons of annual cycles in surface temperatures.  The others, by topic, were:

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
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34 Responses to In Honor of the 4th of July, A Few Model-Data Comparisons of Contiguous U.S. Surface Air Temperatures

  1. Bob Tisdale says:

    PS: Happy 4th of July for those who are celebrating. For the rest of the readers, Happy Monday.

  2. dogdaddyblog says:

    Bob, happy These United States’ Independence Day to you, too. Thank you for your continued cogent work. Without it I would not know of the various oceanic thermal processes. As always, I encourage others to contribute money (as I do) to your efforts.

    Have Gavin Schmidt or any of the other modeling gurus made any substantive response to your many model vs. reality observations? I’m aware of a few of Mr. Schmidt and others’ sophistic pronouncements, but nothing of substance.

    Dave Fair

  3. Bob Tisdale says:

    Dave, for the most part, they stay away from commenting about blog posts.


  4. dogdaddyblog says:

    Thanks, Bob. What about your books and open letters, especially the one to the Royal Society?



    Do men have the ability to effect climate change on the planet earth? Are heat waves, cooling temperatures, earth quakes, hurricanes, tornadoes, floods, droughts, gale-force winds, and snow storms the result of man’s mismanagement of the planet?

    Is it not naive and arrogant to assume that puny man can effect climate change? Man-made climate change is a grand hoax, at best.


    Genesis 6:13 The God said to Noah, “The end of all flesh has come before Me; for the earth is filled with violence because of them; and behold, I am about to destroy them with the earth.

    Were men responsible for the change in the weather pattern? Yes. Did men effect the change of the weather? No, God caused it to rain for 40 days and 40 nights, not men.

    Genesis 18:20 And the Lord said, :The outcry of Sodom and Gomorrah is indeed great, and their sin is exceedingly grave.

    Genesis 19:24 Then the Lord rained on Sodom and Gomorrah brimstone and fire from the Lord out of heaven,

    Were men responsible for the weather change in Sodom and Gomorrah? Yes. Did men cause the weather to change? No, God effected the change in the climate.

    1 Kings 8:35 “When the heavens are shut up and there is no rain; because they have sinned against You….

    Men are sometimes responsible for droughts, however, God effects the weather changes.

    Matthew 27: 51,54 And behold , the veil of the temple was torn in two from top to bottom; and the earth shook and the rocks were split. 54 Now the centurion, and those who were with him keeping guard over Jesus, when they saw the earthquake and the things that were happening, became very frightened and said, “Truly this was the Son of God!”

    Notice, the centurion did not attribute the earthquake to man-made climate change.

    2 Peter 3:10 But the day of the Lord will come like a thief, in which the heavens will pass away with a roar and the elements will be destroyed with intense heat, and the earth and its works will be burned up.

    Men are responsible for the global warming that is coming.

    No amount of green initiatives will stop the final global warming. Puny men will have no effect on the final climate change.



    a blog about Christian doctrine

  6. Bob Tisdale says:

    Dave, same thing applies to the books and my open letter to the RMS.


  7. dogdaddyblog says:

    Bob, I note that some, such as Curry, have been invited before Congressional committees. Have you ever had such an opportunity? Any speaking engagements (e.g. Heartland Institute)? I would like to hear you in person if you make presentations in the future.


  8. Bob Tisdale says:

    Thanks for your interest, Dave. I haven’t given a public presentation in more than 2 decades, and I don’t have any planned in the future. Recall WUWT-TV? I sent Anthony a prerecorded video. I was the only person not to speak live. Stage fright and lack of practice are my excuses.


  9. dogdaddyblog says:

    Bob, I’m an internet novice so I will search for WUWT-TV for your video. I totally understand your hesitation to step into the limelight, but us old dudes must hang together and encourage effective joint action. If you are ever tempted to “go public,” I’ll coach you. I’ve had to perform in public a great number of times and have learned some tricks. Hell, I’ve nothing but time. What’s a plane trip?

    I took a look around the web and uncovered some reactions to your work. Have you jilted Hotwhopper (Sue)? Did you leave her at the alter? Her vituperation and personal attacks against you are beyond anything I have ever seen. She and others express contempt that you do not have some sort of academic degree, or have not worked in some sort of “climate science” discipline. They express disdain of people who are self-taught in the complex but fairly straightforward manifestations of climate in the physical world.

    You are criticized for being a “citizen scientist.” I understand you are retired, from what I assume was a productive career. I, too, am retired but my mind (as I assume is yours) is as sharp as it was at my career peak. We do not leave our intellect and reasoning ability behind in our old careers. Our understanding of complex systems and processes are as good as or better than those of purported climate experts. Data (empirical facts) are everything! Intelligent people of any stripe can analyze objective facts and postulate reasoned conclusions about the state of our environment.


  10. Bob Tisdale says:

    Thanks for the kind words, Dave.

    Miriam O’Brien (aka Sou) simply hates anyone who disagrees with her opinions on climate.


  11. Allan MacRae says:

    Hi Bob,
    Wanted to email you but cannot find your address.
    Perhaps you missed this post. Can you comment on it – publicly or privately? .
    Best, Allan

  12. Bob Tisdale says:

    Allan, thanks for the heads-up. Here’s my reply on that WUWT thread

    Allan, there should be no doubt that much of the mid-20th Century cooling was the result of natural variability, primarily due to the naturally occurring multidecadal variations of the sea surface temperatures of the Northern Hemisphere and polar amplified cooling…not aerosols.

    PS: Nice to see Leif playing devil’s advocate in a comment about yours in your first link here.

  13. Allan MacRae says:

    Thank you Bob,

    I replied to Leif here:

    I cannot understand how he could so completely misunderstand my comments.

    The model-cooking using fabricated aerosol data is fraudulent, imo.

  14. Allan MacRae says:

    Thank you Bob. Did you see this post?

    I plotted the same formula back to 1982, which is where I started my first analysis. Satellite temperature data began in 1979.

    That formula is: UAHLT Calc. = 0.20*Nino3.4SST +0.15
    It is apparent that UAHLT Calc. is substantially higher than UAH Actual for two periods, each of ~5 years, BUT that difference could be largely or entirely due to the two major volcanoes, El Chichon in 1982 and Mt. Pinatubo in 1991.

    This leads to a startling new hypothesis: First, look at the blue line, which shows NO significant global warming over the entire period from 1982 to 2016. Perhaps the “global warming” observed after the 1997-98 El Nino was not global warming at all; maybe it was just the natural recovery in global temperatures after two of the largest volcanoes in recent history.


    Regards, Allan

  15. Bob Tisdale says:

    Allan, what are the trends of your “UAHLT Calc.” from 1996 to present and the UAH TLT data for the same time period.

  16. Allan MacRae says:

    Hi Bob,

    Re your question:

    From (about) 1Jan1982 to (about) now:
    – the UAHLT trendline is 0.0131C per year which is 0.131C per decade, or about 0.1C per decade.
    – the UAHLTCalc trendline is 0.0002C per year which is 0.002C per decade –essentially zero.
    – The slope difference is largely due to the two volcanoes El Chichon and Mt. Pinatubo.

    From (about) 1Jan1995 to (about) now:
    – the UAHLT trendline is 0.0064C per year which is 0.064C per decade.
    – the UAHLTCalc trendline is 0.0043C per year which is 0.043C per decade.
    – slopes from 1Jan1996 will be even closer since the greatest difference is in 1995.

    I will send you the spreadsheet if you send me your email – I suggest via Anthony, who has both.

    Regards, Allan

  17. Bob Tisdale says:

    Thanks, Allan. No need to send me a spreadsheet. When I get a chance, it’ll only take me a few moments to confirm your results. Mind if I prepare a post if my results are similar to yours to bring more attention to your findings? Of course I’d give you credit.


  18. Allan MacRae says:

    Yes of course Bob – please proceed and let me know how it turns out..

    BTW, WillisE responded with a “so what?” on my facebook page, excerpted below, where I posted the graphs (because I had to post them somewhere – I do not use facebook). I am unsure if Willis used the same methodology that I did.

    However I think this correlation is significant, and may suggest causation – with only about 1% of the planet’s surface area (Nino3.4) showing a strong correlation with UAHLT 4 months later.

    I also received a more positive response from John Christy, as follows:
    “The tropical Pacific is very much a player in global temps. Attached is a paper we did in 1994 that used this fact. We’ve updated it using more SST and circulation modes, and hope to see it published later this year.”
    John refers to a paper in Nature, Vol.367, p.325, 27Jan1994 that he co-authored with Richard McNider.

    You may also be interested in this paper I wrote in January 2008, in which I stated:

    Click to access CO2vsTMacRae.pdf

    “The rate of change of atmospheric CO2 (dCO2/dt) correlates closely and ~contemporaneously with global temperature, and its integral atmospheric CO2 lags temperature by about nine months in the modern data record. CO2 also lags temperature by ~~800 years in the ice core record, on a longer time scale. Therefore, CO2 lags temperature at all measured time scales.

    Now we have global UAHLT temperature lagging NIno3.4 by about 4 months, so LT temperature presumably lags Nino3.4 by what, (4+9=)13 months? There must be a simple solution to all this – wish I had more time…

    Consider the implications of all this evidence:
    CO2 lags temperature at all measured time scales, so the global warming (CAGW) hypothesis suggests that the future is causing the past. Hmm…. 🙂

    Also see this:

    Best, Allan

    Here are Willis’ comments, with a few of my responses:

    Since the Nino3.4 index is nothing but the temperature of a significant chunk of the world surface … why would you expect anything different? That’s as exciting as saying “I can calculate the temperature of my kitchen if I know the temperature of my living room”! Yes, you can … but so what? w.


    Thanks, Allan. You’ve missed my point, likely my fault from my lack of clarity. Allow me to try again.

    You seem to put a lot of weight on the fact that the El Nino area, which is a part of the planet, generally tracks the temperature of the planet.

    I don’t see why this is significant. Yes, the Nino3.4 index and the globe generally move at least somewhat in harmony … but so what? So does much of the surface. What does that tell us we don’t know already?

    As a counter-example, the correlation with global temperatures is even better in the tropical north atlantic, which generally doesn’t display “El Nino” type activity … but again, so what?

    I do like the analogy of Kansas, and I plan to see which parts of the planet are most strongly correlated with US temperatures two months later. I’ll report back

    For those who haven’t read it, you might enjoy my post “Weather Two Months From Now” linked below.

    PS—If you look at the map in the post below, you’ll see that most points on the planetary surface are positively correlated with global temperature a couple of months later … just as we’d expect them to me. Which is why the fact that the El Nino region is positively correlated with weather two months later is not that impressive to me—most of the surface is positively correlated with global weather two months later.…/weather-two-months-from-now/
    Weather Two Months From Now
    Guest Post by Willis Eschenbach A while back, folks noticed that a couple of months after the El Nino kicked…


    A good post that I missed, thank you Willis. You used a 2-month lag whereas I found a 4-month lag was a better fit – did you try 4 months? You may find the NH fit is better at 4 months and the best-fit location is different (or not). As I said previously, the oceans have reportedly warmed but the Nino3.4 area has not, on average. Happy July 4.


    Thank you Willis. John Christy just sent me the following comment on this topic.

    The tropical Pacific is very much a player in global temps. Attached is a paper we did in 1994 that used this fact. We’ve updated it using more SST and circulation modes, and hope to see it published later this year.
    John C.”

    John refers to a paper in Nature, Vol.367, p.325, 27Jan1994 that he co-authored with Richard McNider.

    They used the combined areas Nino 3 and Nino 4 (about twice the area of my Nini3.4) and said the delay of UAHLT after Nino3+4 was 5 months vs my 4 months.

    Best, Allan

  19. Allan MacRae says:

    FYI Bob – this may save you some time.
    Best, Allan

    My formula is: UAHLT Calc. = 0.20*Nino3.4SST +0.15
    Nino3.4 is the temperature anomaly in degrees C of the SST in the Nino3.4 area, as measured by NOAA in month m. Nino3.4 comprises about 1% of the Earth’s surface area;
    UAHLT is the Lower Tropospheric temperature anomaly of Earth in degrees C as measured by UAH (University of Alabama Huntsville) in month (m plus 4) – four months in the future.

    Plotted at

  20. Bob Tisdale says:

    Allan, regarding Willis’s comment, it’s well known that the large monthly variations in lower troposphere temperature data are responses to ENSO, so he may have been trying to say that your blog comments really aren’t telling us anything new. Willis’s comment was simply more blunt than you would have liked.

    I took a look at the relationship between NINO3.4 and UAH TLT v6.5 data starting in 1996. I could try to justify the use of 1996 as a start year because it’s 5 years after the eruption of Mount Pinatubo and a year before the 1997/98 El Nino.

    The scaling factor I used for adjusting the NINO3.4 data was 0.18. It was based on the standard deviations of the TLT and NINO3.4 data from 1996 to now. The trend of the adjusted NINO3.4 data (your UAHTL Calc) was 0.056 deg C/decade versus 0.064 deg C/decade for the UAH TLT data. Lagging the adjusted NINO3.4 data by 4 months drops its trend to 0.05 deg C/decade. Now, we could say that for the period starting in 1996, roughly 80% of the TLT warming rate is dependent on ENSO.

    BUT (big but)

    1998 is a more commonly used start year for the global warming pause or hiatus. Using 1998 as the start year changes the relationship between the TLT and the adjusted NINO3.4 data in a big way. The UAH TLT data show a trend of only 0.03 deg C/decade, while the adjusted NINO3.4 data show a trend that’s 3 times higher at 0.09 deg C. We can no longer say that there is a trend relationship between the two.

    Also, for those relationships, I used NINO3.4 data based on the standard NOAA Reynolds OI.v2 data, which I believe is the dataset you were using. If I use the NOAA ERSST.v4-based NINO3.4 data, the relationship changes. Starting in 1996, the trend of the adjusted NINO3.4 data (0 lag) drops to 0.04 deg C/decade, so now the trend of the adjusted NINO3.4 data are only representing about 66% of the UAH TLT trend.

    Basically, you’re trying to establish a relationship in warming rates between an adjusted (scaled) ENSO signal and UAH TLT data but those relationships are highly dependent on the start year and on the sea surface temperature dataset used for the NINO3.4 data.

    In other words, it would be difficult at best to try to establish how much of the warming of TLT data in recent years was simply due to ENSO when the relationships depend on the start years and on the sea surface temperature dataset used for the NINO3.4 data.


  21. Allan MacRae says:

    Hi Bob.

    Thank you for your comments.

    Can you please post your graph so I can comment on it? If you did not use the same data as I did, please do so.

    I do not regard your comments re the average slope as particularly important – I will comment later after I see your graph. I also cannot comment on Willis’ opinion because I do not understand if his methodology is similar or different from mine. That this correlation is not new is no surprise – John Christy also said that, based on his 1994 paper. The fact that John is updating his paper suggests it is significant.

    In my opinion, the strong correlation of Nino3.4 temperature (1% of the planet ‘s area) with global Lower Tropospheric (LT) temperature four months later does demonstrate a probable causative mechanism for short-term temperature change on the entire planet. The existing IPCC climate models fail this and other tests.

    I have already demonstrated conclusively in Jan2008 (reference below) that atmospheric CO2 lags global Lower Tropospheric temperature by 9 months in the modern data record. This is the only clear correlation that I found in the data. CO2 also lags temperature by about 800 years in the ice core record, on a longer time scale. This 2008 correlation suggests to me, at a minimum, that temperature drives CO2 more than CO2 drives temperature. It does not necessarily mean, as some have suggested, that temperature is the only or the primary driver of atmospheric CO2 – other drivers of increasing CO2 could include fossil fuel combustion, deforestation, etc.

    It is also probable that the IPCC climate models have cause-and-effect reversed, such that atmospheric CO2 variation is (at least in part) an effect rather than a cause of temperature change.

    The period of global cooling from ~1940-1975, even as global fossil fuel combustion rapidly accelerated, further demonstrates that the IPCC models are wrong and ECS, the sensitivity of global temperature to increasing atmospheric CO2 is very low – so small as to be insignificant.

    I conclude that we have in the UAHLT vs. Nino3.4 correlation a mechanism that provides a credible understanding of what drives short-term global temperature variation, and that mechanism has far greater credibility than any CO2-driven climate model. The evidence suggests that this correlation will hold true into the future except when major volcanoes occur.

    I believe that long-term temperature variation is driven by other factors such as solar variability, and in the longer term by other planetary variables. I have seen some credible models that show this relationship, but have not derived any myself.

    Best personal regards, Allan

  22. Allan MacRae says:

    Re your question on trends – I gave you from 1982 and 1995 and you requested from 1996:

    From 1Jan1996 to (about) now:
    – the UAHLT trendline is 0.0068C per year which is 0.068C per decade.
    – the UAHLTCalc trendline is 0.0062C per year which is 0.062C per decade.
    – Essentially the same.
    – Even if the trends were different, I suggest this is not the critical parameter.
    – The critical parameter is how well the plots follow each other in detail, and that is obvious.

  23. Bob Tisdale says:

    Allan, per your 2:37 PM comment.

    I’ve updated my graph with the data through June. That changed the trends slightly. I did not use the scaling factor you used. I determined mine by dividing the standard deviation of the TLT anomalies by the standard deviation of the NINO3.4 SST anomalies.


    PS: Regarding your 8:11 PM comment, I had already determined the trends since 1996 and provided them in my last reply.

    PPS: As I noted in my earlier reply to you, the processes which cause the relationship between ENSO and global TLT anomalies are well known. To speak bluntly, all you’re doing is confirming a relationship that has been understood for decades. You are not furnishing anything new.


  24. Allan MacRae says:

    For completeness, here is the correct plot for my 811pm post.

  25. Allan MacRae says:

    Bob, you said above:
    “Mind if I prepare a post if my results are similar to yours to bring more attention to your findings?”

    From you statement, it appears that you were as unaware as I was of these findings. Not surprising, since I have studied this subject for years and only now became aware of John Christy’s 1994 paper in Nature, and only because John sent it to me.

    I suggest that very few people were aware of this very close relationship between Nino3.4SST and UAHLT four months later, and I suggest that relationship is significant.

    To be clear, there has long been talk about the impact of El Nino on global temperature – especially the big temperature spikes like 1997-98, but I had seen nothing until now about the very close relationship of these two parameters, and the ~four-month lag of UAHLT after Nino3.4 SST.

    Regards, Allan

  26. Bob Tisdale says:

    Allan says: “From you statement, it appears that you were as unaware as I was of these findings.”

    And you may be reading more into the comment than I had intended. I was hoping to find something new, but it wasn’t there. If the relationship in trends between the adjusted NINO3.4 data and UAH TLT data had remained somewhat constant, then it would have been worthwhile to prepare a post, but the relationships had not remained constant…they, as noted in an earlier comment, reversed when I used 1998 as the start year.

    Allan says: “To be clear, there has long been talk about the impact of El Nino on global temperature – especially the big temperature spikes like 1997-98, but I had seen nothing until now about the very close relationship of these two parameters, and the ~four-month lag of UAHLT after Nino3.4 SST.”

    Then you haven’t looked very hard Allan. See my 7-year-old post here:

    And my 2010 post here:

    Wanna watch an animation of TLT anomaly maps in response to the 1997/98 El Niño? See the animation here (the graph has nothing to do with the TLT Data) that I created over 5 years ago:

    The Animation is from the post here:

    And for one of the favorites of the warmistas, there’s Foster and Rahmstorf 2011:


  27. Allan MacRae says:

    Thank you Bob,
    Thank you for your references, especially from 2009 and 2010.
    Regards, Allan

  28. Pamela Gray says:

    My thoughts:
    The trends may “dis-correlate” from time to time because of the vagaries of short term discharge/recharge episodes and the stochastic behavior of weather. Any series of data that is both stochastic at short and multi-decadal term levels, but oscillatory at millennial term levels will have variable correlation depending on the length of time and start/end points.

    As an example (but a poor one because I can’t come up with another one quickly), educational data (learning over time) is similarly correlated with episodes of dis-correlation. We know that from kindergarten to 12th grade, students continue to gain more complicated academic skills. However, when looking at any given year, particularly at the end points, growth is not as evident, if at all. Indeed, at times it looks like average students don’t progress, or even lose skills, if only one year is considered.

    Climate change, if thought to be naturally derived, should be treated as internally correlated (IE internal cause and effect mechanisms) over the long term with stochastic dis-correlated short term episodes. With that as the basis, certain statistical methods when looking for millennial term correlation should not be used when examining relationships that by its nature dis-correlate at short term spans due to stochastic behavior.

    The above discussion of Allan’s work involving small changes in starting points in a very short segment of climate places the research at risk of type 1 errors: rejection of what is in reality, a true null hypothesis (in this case equatorial ocean discharge/recharge processes correlate with and cause subsequent land temperature).

    The discussion highlights this: The process of “a priorially” narrowly defining your research topic, choosing your methods, and applying appropriate statistical mathematics is paramount to good research and discussion. It also keeps my good friend Willis from saying, “So what?”

  29. Pamela Gray says:

    To clarify, if the null hypothesis is that internal cause and effect processes cause millennial term trends, but short term segments are used to reject the null hypothesis, a type 1 error may be committed.

  30. Allan MacRae says:

    Thank you Pamela for your comments.

    The short-term correlation between NIno3.4 SST and UAHLT temperatures ~four months later is robust relationship, absent the occurrence of major volcanoes that caused global cooling (for up to ~five years in the two recent examples). It is, however, a short-term correlation that does not address longer term causes and effects of Earth’s changing climate.

    It is interesting that atmospheric CO2 lags global average temperature at all measured time scales, from ~9 months after UAHLT in the modern data record to ~800 years in the ice core record, on a longer time scale. When I first published my observation in January 2008, it was strongly rejected as “spurious correlation”. Later this lag was accepted but dismissed as a “feedback effect”. I suggest that both these negative responses were false.

    Click to access CO2vsTMacRae.pdf

    I suggest that temperature (among other factors) drives atmospheric CO2 much more than CO2 drives temperature. This does not preclude other drivers of CO2 such as fossil fuel combustion, deforestation, etc. but the impacts of increasing CO2 are overwhelmingly positive for humanity and the environment. In conclusion, there is no global warming crisis, and CO2 reduction and abatement schemes are costly and destructive nonsense.

    I have also written since at least 2009 that atmospheric CO2 is not alarmingly high, it is alarmingly low for the continued survival of carbon-based life on Earth. I continue to hold this belief as essentially correct.

    My primary concern in this matter is the obsession of our current crop of politicians with nonsensical energy policies. The assumption that they can replace reliable, cheap fossil fuels with intermittent, costly “green energy” schemes is delusional, and will lead to rising energy costs and increases in Excess Winter Mortality, especially among the elderly and the poor.

    Best personal regards, Allan

  31. Allan MacRae says:

    Trying again for the above plot:

  32. Pingback: NOAA’s New Climate Explorer – NOAA Needs to Provide a Disclaimer for Their Climate Model Presentations | Bob Tisdale – Climate Observations

  33. Pingback: NOAA’s New Climate Explorer – NOAA Needs to Provide a Disclaimer for Their Climate Model Presentations | Watts Up With That?

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