Man, I Hate Being Wrong

Guest Post by Willis Eschenbach

We’ll get to what I did wrong in a moment, but first, in my last post, a number of folks questioned my calculation of the surface temperatures from the CERES surface dataset. This is a dataset which is calculated from the CERES measured top-of-atmosphere (TOA) radiation measured by the satellites.

Let me start with a description of the CERES surface dataset from the developers:

EBAF-Surface Product Features

Global gridded, monthly mean surface fluxes calculated using a radiative transfer model.

Radiative transfer calculations are performed hourly on the CERES 1° equal-area grid.

Cloud properties are derived from narrowband imagers onboard both EOS Terra and Aqua satellites as well as Geostationary satellites to more fully model the diurnal cycle of clouds.

Gridded monthly mean cloud and atmospheric properties are adjusted so that model results:

• approach CERES net balanced Top-of-Atmosphere fluxes (EBAF-TOA product), where the global net is constrained to the ocean heat storage.

• more closely match modeled downward longwave surface fluxes that include active cloud base measurements from Calipso and Cloudsat.

Clear sky is separately adjusted to the monthly mean from CERES EBAF-TOA clear-sky ‘filled’ observations.

And here’s a flowchart showing how they get from the CERES measured TOA radiances to the surface datasets.

Figure 1. Flowchart showing how the CERES surface radiation datasets are calculated from the top-of-atmosphere (TOA) radiation measurements.

There is a lot more information about the calculation of the surface dataset where those came from. And for folks who want to see how the sausage is made, there’s a deeper dive into the process at Estimation of Longwave Surface Radiation Budget From CERES.

The CERES surface datasets include a dataset of the upwelling longwave from the surface. But that’s not much use to me. I wanted surface temperatures rather than surface upwelling longwave emissions. However, the Stefan-Boltzmann equation lets us convert from longwave emission to temperature if we know the emissivity.

The good news is that for natural substances, in almost all cases the emissivity is quite close to 1.0. Here’s a list of a few emissivities:

Water, 0.96
Fresh snow, 0.99
Dry sand, 0.95
Wet sand, 0.96
Forest, deciduous, 0.95
Forest, conifer, 0.97
Leaves Corn, Beans, 0.94

and so on down to things like:

Mouse fur, 0.94
Glass, 0.94

You can see why the error from considering the earth as a blackbody in the IR is quite small.

I must admit, though, that I do greatly enjoy the idea of some mad scientist at midnight in his laboratory measuring the emissivity of common substances when he hears the snap of the mousetrap he set earlier, and he thinks, hmmm … but I digress.

In any case, since I did not know the actual emissivity of the various surfaces, I decided to use 1.0 for the emissivity of all of them. I reasoned that it would only make a slight difference in the absolute temperatures, and it would not affect relative temperatures or trends at all.

So … how well does my converted dataset agree with the other global datasets? Since the ocean is 70% of the surface, let me start by comparing the converted CERES data to the Reynolds Optimally Interpolated sea surface dataset. Here is the average ocean temperature per Reynolds, minus the average ocean temperature per converted CERES.

Figure 2. Average Reynolds Optimally Interpolated Sea Surface Temperature minus average converted CERES temperatures. Averages are over the period of the CERES datasets, March 2000 to February 2018.

They are nearly identical almost everywhere … so why the difference at the poles? It’s because the Reynolds OI SST is showing the water temperatures, and the CERES data is showing the ice temperatures at the actual surface … so the CERES data is much colder at the poles. If we omit everything above and below the Arctic and Antarctic circles, we get the following:

Figure 3. Average Reynolds Optimally Interpolated Sea Surface Temperature minus average converted CERES temperatures. Averages are over the period of the CERES datasets, March 2000 to February 2018. Areas polewards of the Arctic and Antarctic circles have been excluded.

Note that there is much less than one degree of error in the averages. The agreement over the ocean is impressive.

Next, the land. I compared the converted CERES data to the HadCRU land-only data and the Berkeley Earth land-only data, along with the MSU UAH land-only satellite lower troposphere temperatures. Unfortunately, those three datasets are temperature anomalies, not absolute temperatures. So I couldn’t do a comparison of absolute values as I could with the Reynolds OI SST dataset. In lieu of that, here are the annual changes in the anomalies of the four land-only datasets …

Figure 4. Anomalies, land-only global temperature averages, from Berkeley Earth, converted CERES, HadCRUT, and the University of Alabama Huntsville Microwave Sounding Unit (UAH MSU) lower troposphere temperature.

The pairwise correlations of the datasets are quite similar, with the expected exception of the UAH MSU lower troposphere temperatures. These UAH MSU temperatures are measuring the lower troposphere and not the surface, so they are smoother and they don’t correlate quite as well with the other surface temperature datasets.

         Berkeley CERES HadCRUT UAH MSU
Berkeley       NA  0.88    0.89    0.85
CERES        0.88    NA    0.86    0.80
HadCRUT      0.89  0.86      NA    0.77
UAH MSU      0.85  0.80    0.77      NA

With that as prologue, I titled this post “Man, I Hate Being Wrong”. It has that title because my last post contained wrong calculations. So … where was I wrong in my last post? The error was not in the conversion of the CERES surface radiation data to temperatures as some people thought. That calculated temperature dataset, as shown above, is quite close to the other global ocean and land temperature sets.

Where I went wrong was in the calculation of the individual trends. Last week I’d written a new algorithm to calculate trends. And I thought that I’d tested it … but I’d tested it without remembering that trends in sinusoidal datasets are heavily affected by the choice of endpoints. Grrr … wrong again. So the main graphic in my last post is incorrect, with all of the trends being too low by about a tenth of a °C/decade. That definitely angrifies a man’s blood.

Here is the correct analysis of the decadal trends in the temperatures around the globe:

Figures 5a and 5b. Temperature trends for the period March 2000 to February 2018, correctly calculated this time.

Finally, and perhaps not surprisingly, now that I have the correct results, the trends of the converted CERES data and the two surface-station-based datasets are different. The trend of the CERES dataset is much closer to the trend of the UAH MSU lower troposphere dataset. Here are the decadal trends, land-only:

Berkeley Earth: 0.27 °C/dec

HadCRUT: 0.29 °C/dec

UAH MSU: 0.17 °C/dec

Converted Ceres: 0.16 °C/dec

And here are the trends of the ocean data:

Reynolds OI: 0.12 °C/dec

Converted Ceres: 0.14 °C/dec

UAH MSU: 0.11 °C dec

This agreement between the trends of the converted CERES temperature data with those two oceanic datasets strongly suggests that the land-station based datasets are trending high … the CERES dataset agrees with the UAH MSU on both land and sea, and with the Reynolds UI SST at sea.

Where is the difference in trends between CERES and Berkeley Earth? As you might expect, it’s generally where there are fewer surface temperature stations—the upper Amazon, central Africa, the Arctic …

Figure 6. Difference in trends, Berkeley Earth trends minus converted CERES trends.

So my main conclusions are:

My previous post sucks. As much as I’d like to just ignore it and move on, I’m not built that way. I’m sworn to tell the truth as I best know it in all my posts, and that means admitting when I’m wrong even when no one else notices where the mistake is located. In this case, I was 100% wrong. Regarding that post, pay no attention to that man behind the curtain.

 This analysis agrees with my earlier comparisons of the converted CERES temperature dataset with other datasets. The absolute values and the month-to-month variations in my conversion of the CERES surface longwave radiation data to temperature are very close to the other temperature datasets (Reynolds OI SST, Berkeley Earth Land Only, HadCRUT Land Only, and UAH MSU lower troposphere land and sea).

So I am at ease using the converted CERES data as a valid temperature dataset. However, the trends are different from the surface-station datasets … and that has almost nothing to do with the exact conversion from radiation to temperature—trends in one will be trends in the other.

The trend of the CERES temperatures is quite similar to the MSU UAH trend over both land and sea, and to the trend of the Reynolds OI sea surface temperature. I suspect that this indicates that the station-based land trends are gradually affected over time by encroaching civilization—more blacktop, more roads, more auto traffic, more jet exhaust and more powerful jet engines at the airport stations, more air conditioners, more sewage plants, more buildings, taller buildings, the list of things that can bias temperatures upwards is long.

Man … I hate being wrong.

My very best wishes to everyone, with hopes that y’all can avoid being publicly wrong, it’s no fun at all.

w.

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139 thoughts on “Man, I Hate Being Wrong

  1. Darwin said ( more or less) that making an error in analysis is no deterrent to the advancement of science because your friends will regale themselves pointing out the error. False” facts” however set science back because of the time it takes to accumulate the appropriate data to overcome the falsification. Merry Christmas!

  2. ” I suspect that this indicates that the station-based land trends are gradually affected over time by encroaching civilization”

    Willis – some grist for the mill: How about varying cloud formation over land (as compared with oceans) as temperature slowly rises?

    Cheers

    M

    • How about land use, swamp draining, and irrigation.
      There are a lot of weather stations in the economically important agricultural regions.

  3. Don’t ever stop digging into the data, and fearlessly tossing out ideas. Every now and then you’ll have one of these, but hey, there is nothing wrong with showing your thought process in public. If everyone was as brave, we’d be far better off.

    • I’m working on a Mac, and I use an app called “Snapz Pro X”. I set the options to “Drop shadow” and “Rounded corners”.

      If you’re on a PC, there’s a discussion of alternatives to Snapz here.

      Regards,

      w.

      • Oh I actually meant how do you make the picture of the earth with the colours. If I understand “Snapz” is just for taking the screenshot.

        • Indeed, Jarryd, Snapz is for the screenshot.

          The actual graph is done using the computer program “R”, the world’s best computer language. I’ve written a raft of special programs that handle a variety of things related to displaying map data. At this point, since they’re all written, it’s easy, I just say “drawworld(mymap)” or the like … but the writing of the code took a while …

          If you have any computer skills, I strongly recommend learning R. I did so at the urging of Steve McIntyre and it has made my research not only possible but fun.

          w.

          • Ah right, thanks. They are rather impressive. I’m a software engineer, and I am somewhat familiar with R, I helped my wife doing her PhD with it actually. It’s quite a bit different to anything else I had ever used before.

            I played around with some temperature series of my local area with R myself, it was rather illuminating. Let’s just say it didn’t show unprecedented warming.

  4. If you don’t make any mistakes you haven’t tried hard enough or risked enough!

    I know of one company where the research team had a gong in the hall. A notable moment was two gongs, but a failed experiment was also a gong. If the boss didn’t hear enough gongs then he would want to know why.

    • That would be me … I noticed it when I started doing the analysis and comparison of the CERE data to the other datasets.

      w.

          • Michael, that comment by Mosh was totally understandable. What’s the issue?

            And what is it with you guys hating on Mosh? Give it a rest! He’s a very smart man, ignore him at your own peril. I don’t like his laconic posting style, but I can generally piece together what he’s saying, and I often learn important things from what he says.

            w.

          • Willis,

            You can’t win for losing! If you give a long answer to a simple question, for free of course since none of these guys pay for you or me or charles to work, they will either change the topic or ignore the fact that you answered their question. And if you give a short answer which is still a pay per word less than poetry, they will accuse you of being a drunk.

          • Willis, any person claiming what they are defending is error-free and justifies coercion of billions of people should be treated roughly! Every day every area of science admits errors, frauds, badly programmed models and so on, every day papers are retracted, argued over, results reproduced (or not) EXCEPT in climate science and except by people like Mosher. And to ask why not gets you called names by Mosher and others

            Being smart is not the same as being right.

        • Ben Vorlich December 7, 2018 at 1:56 am

          Would you have spotted it earlier if the raw data for the datasets was available rather than just anomaly data?

          Nope, that wasn’t the issue.

          Plus, as Mosh pointed out, the Berkeley Earth raw data is available … and the Reynolds OI SST data is in absolute temperature.

          w.

  5. After this analysis how can anyone criticize the UAH dataset? The unfortunate part of it for the skeptic side is that the dataset starts in 1979 a relatively cool period in the last 50 years.

      • Willis,

        Please, Amsterdam at the center (of the world) for Hans and Brussels for me, although as I live near Antwerp, I don’t like (EU) Brussels for capital, they already think the EU and Brussels are the center of the world… /sarc

        Hans follows the European temperatures already for a long time, from the longest series in the world: the CET (central England temperature) and many other long series from places in Europe, to anecdotal evidence about drought, heat waves, etc. over longer periods back in time…

        • Ferdinand Engelbeen

          Scotland is the centre of the civilised world.

          How the Scots Invented the Modern World: The True Story of How Western Europe’s Poorest Nation Created Our World & Everything in It

          An exciting account of the origins of the modern world

          Who formed the first literate society? Who invented our modern ideas of democracy and free market capitalism? The Scots. As historian and author Arthur Herman reveals, in the eighteenth and nineteenth centuries Scotland made crucial contributions to science, philosophy, literature, education, medicine, commerce, and politics—contributions that have formed and nurtured the modern West ever since.

          Herman has charted a fascinating journey across the centuries of Scottish history. Here is the untold story of how John Knox and the Church of Scotland laid the foundation for our modern idea of democracy; how the Scottish Enlightenment helped to inspire both the American Revolution and the U.S. Constitution; and how thousands of Scottish immigrants left their homes to create the American frontier, the Australian outback, and the British Empire in India and Hong Kong.

          How the Scots Invented the Modern World reveals how Scottish genius for creating the basic ideas and institutions of modern life stamped the lives of a series of remarkable historical figures, from James Watt and Adam Smith to Andrew Carnegie and Arthur Conan Doyle, and how Scottish heroes continue to inspire our contemporary culture, from William “Braveheart” Wallace to James Bond.

          And no one who takes this incredible historical trek will ever view the Scots—or the modern West—in the same way again.

          https://www.amazon.com/How-Scots-Invented-Modern-World/dp/0609809997

          🙂

          • Herman must be a Scot. The Scottish Enlightenment is impressive but there are others equally impressive before it or at the same time: The Italian Renaissance, English scientific revolution and the French Enlightenment. Great figures:

            Italian Renaissance: Brunelleschi, Donatello, Da Vinci, Michelangelo, Machiavelli, Vesalius, Cardano, Bombelli, Bruno, Galileo

            English scientific revolution: Royal Society, Francis Bacon, Gilbert, Harvey, Boyle, Hooke, Newton, Halley, Bayes, Cavendish, Priestley, William Hershel, Caroline Herschel

            French Enlightenment: Voltaire, de Moivre, Maupertius, Diderot, D’Alembert, Emilie du Chatelet, Lagrange, Laplace, Lavoisier, Legendre, Charles, Coulomb, Fourier

          • Have a look at this : Swords at Sunset
            A three-year investigation of the site undertaken at the request of Nova Scotia’s Ministry of Culture, Recreation and Fitness indicated that the structure had most probably been built by religious refugees in AD 1398.
            This colony of religious refugees had been established by “Prince” Henry Sinclair, Baron of Rosslyn in Scotland. Henry was also Earl of Orkney, a title and domain he held in fief to the king of Norway.

          • Bonbon
            Thank you for that link as a Sinclair I’m always happy to read of our family/clan exploits.

          • Well Hot Scot; as one who moved from England to Scotland (and then went on to Canada), I am well aware of how Scotland and the Scots did far more than their share of the intellectual heavy lifting in the age of enlightenment. In my own field of geology, the work of James Hutton and colleagues in Edinburgh (tectonics and magmatism) did equally as much as the work of William Smith in England (paleontology and stratigraphy) in England to create the science of geology as we know it.

            And on and on.

            On arriving in Scotland (where I felt more at home than I ever did in my native country), I was initially impressed by the ideas of John Knox (of whom I’d never heard before) that everyone should be free to pursue his (or her, although of course that wasn’t a permitted thought at the time) path to enlightenment or redemption. Even as an atheist, I could see merit in the concept.

            It wasn’t long before I came to realise that, in practice it meant that everyone had to follow Knox’s own path to redemption and enlightenment, via the suppression of any activity that might be remotely construed as pleasurable or entertaining. Or else, eternal damnation with no chance of appeal. Five hundred years of ignorance, misery, self-denial and joylessness; that was the price that Scotland paid for its intellectual, educational, industrial and colonial achievements. Was it a bargain? Perhaps.

            Freedom from the church of Rome led only to the tyranny of the Kirk and its various offshoots, each more misery-inducing than the last. The whole edifice was well on the way to crumbling by the 1960s, although it was still alive and well in parts of the highlands and islands. It’s just about faded to nothing now. Good riddance.

            Also, I do beg to differ on the statement that the ideas of John Knox helped to lead to modern democracy. There were good models of democracy in ancient Greece and Rome, and contributions from England (magna carta? peasant’s revolt?). I haven’t seen any evidence that pre-1707 Scotland was any more democratic than England (but I’m receptive to contrary information if you have any).

            If I ever retire, it will be to Scotland, because I still feel at home there. There are still a few hills that aren’t covered in wind turbines. Yet.

      • Willis, for me: please an Atlantic centered map for figure 5.

        Perhaps it is a good idea to produce always two views of the world maps. Important information on the North Atlantic/Arctic region (very important for the Earth’s climate) stays ‘hidden’ in the Pacific projection.

        And for the rest: I am very happy with the data of this post. Thank you very much! I will come back on this.

        • Wim, I’ve added the following graph to the head post:

          Regarding the Arctic and Antarctic views, I have never written the code to do that, and it likely will not be simple. Let me take a look at that and see what it will take.

          Best regards,

          w.

          • Thank you, Willis. The interesting thing for especially the Arctic will be the effects of the inflows of warmer (one degree warmer) subsurface Atlantic water in the nineties and 2000’s. Entering the Arctic between Norway and Svalbard this water follows her 10+ years’ journey along the coast of Siberia to Bering Street and then back to Greenland, cooling herself but meanwhile melting [less and less] the ice above. The extra water vapor in the air (above melted area’s) creates and attracts low pressure areas that import even more humid air from the south. More low pressure area’s are entering the Arctic, even in winter time and the absorption by the extra water vapor gives huge temperature effects especially when very low water vapor used to create very low temperatures during winter by a high direct loss of radiation into space. No delay in energy loss results in direct cooling.

            Because of the ’round trip’ of the warm subsurface waters it would be interesting to see whether patterns of warming are well visible in the Arctic in space and time.

            The whole process would show the natural cause of the N. Atlantic and Arctic warming that is shown by your numbers. No CO2 involved. No GLOBAL warming either (although there is a global temperature effect visible in the averages). The process would also reveal an important cause for the cyclic character of warming periods.

            It is the N. Atlantic / Arctic that is creating the difference. The processes in the oceans are named the Atlantification of the Arctic: after ice melt the Arctic becomes a more integrated part of the Atlantic system(s). Changing air pressure patterns, weather and so climate. Perhaps for a while (decades), perhaps for a longer period.

      • Willis, using today’s offer of ‘a full-service website’, you could also please me very much with maps centered on the Arctic and one centered on the Antarctic region. Both for the data of your figure 5.

        (I suppose the people behind Berkeley and Hadcrut will be interested in Antarctica and Arctic centered maps for figure 6 as well. And to be honest: I am also curious)

      • Willis, all of them,
        One billion people in West Europe and West Africa can’t read your maps for the area they live in.
        Suggestion to bake four maps:
        Pacific centered,
        Atlantic centered,
        north pole centered
        south pole centered.

        • Thanks, Hans. I can do the two, atlantic and pacific. However, the polar are too much programming to be worth it.

          w.

          • Thanks, Mosh. I can do it, I’ve messed with it. It’s just what we used to call a “SMOP” … a small matter of programming.

            When I get time …

            w.

  6. Willis please correct me if I am wrong, but I get the indication from the NASA website that CERES is not controlled by GISS and thus Gavin Schmidt. In the long run, GISS is in danger of becoming irrelevant because I predict that the main people in NASA will stop believing in CAGW and GISS will thus receive less funding while NASA itself will be the goto agency for climate science. Unfortunately NOAA and NCAR are CAGW believers however. If Gavin Schmidt has nothing to do with touching the CERES data, that gladens my heart.

    • CERES is a NASA-Langley project, not connected with GISS. The CERES folks are top-notch, and do a good job given the limitations of the hardware.

  7. Statistics make my head buzz.

    Obviously, even an expert can burn his fingers on it.

    interesting question: did not ask the RESULT for a new check.

    Definitely:

    Thanks for the information.

    / what was really important:

    – there is a Temperature UP to which a process goes on.

    – crossing this Temperature Mark the process stops.

    Even I understood that – and that information was the really important. /

    Great, thanks.

    Regards – Hans

  8. If I am correct the data refers to March 2000 to February 2018, then the converted Ceres values: 0.16 & 0.14 [land & ocean] oC affected by natural variability as the selected period forms the above the average part of the Sine Curve of 60-year cycle. Then, how much is the trend? In this how much is the global warming part of the trend?

    Dr. S. Jeevananda Reddy

  9. Willis,
    On the comparison with USH LT v6.0, I too get a global trend, Mar200-Feb2018 of 0.13C/dec, which compares with your CERES 0f 0.14. Global land and global ocean similar. But regionally it is more erratic:

    UAH “CERES”
    All 0.13 0.14
    NH 0.17 0.24
    SH 0.12 0.04
    Trop 0.15 0.11
    Arc 0.25 0.98
    Aarc -0.06 -0.17
    Land 0.16 0.17
    Ocean 0.12 0.14

    UAH does not support the big contrast between hemispheres that you have.

    • Thanks, Nick. First, I’m not expecting total agreement between the two on all subsets. At the end of the day, they are measuring different variables.

      Second, not sure, but that may relate to a couple of things. One is that UAH is weak over the poles, another is that the lower troposphere retrievals are known to be inaccurate over high terrain. From their files:

      ALSO BE CAUTIOUS USING LT AND MT OVER HIGH TERRAIN ( >1500 M)

      The areas of poor anomaly values are : Tibetian Plateau,
      Antarctica, Greenland and the narrow spine of the Andes.

      In any case … always more to learn.

      Best regards,

      w.

      • Nick, as often is the case your questions inspire me to look further. As you point out there are differences in the trends between MSU and CERES data. I’d speculated that they were from polar differences as well as high altitude land. Here is the situation with those areas removed:

        Without those areas the values are closer.

        Best regards,

        w.

        • Nice work Willis and I agree that Nick Stokes often provides useful and thought provoking comments, even if I don’t always agree. I notice that the high Andes area near the equator seems to show a substantial departure for both CERES vs UAH and CERES vs BEST, but as you note, possibly for differing reasons.

          And speaking of thought provoking, I got to thinking that satellite derived estimates of near surface temperatures may likely be hindered (temporally incomplete) in areas where heavy cloud cover is frequent and thus often obscuring the lower atmosphere. Since temperatures tend to be cooler under heavy cloud cover in the tropics, this effect might cause a slight high bias because of missing the cooler temperatures in the affected tropical areas. The reverse may be true in polar region winters, where near surface temperatures are often not as cold under heavy cloud cover that suppresses radiational cooling. On the other hand satellite estimates may not handle the sharp ground based temperature inversions in clear sky polar winter situations, so it may not matter whether it is cloud or not.

          And speaking of polar region winter, I am not seeing any surface air synoptic buoy reports in the Arctic Ocean recently, unlike several years ago when there were dozens of them. Seems odd that with all the concern about warmer temperatures in the Arctic winter, we now have a dearth of observations over the Arctic Ocean. There are a fair number of buoys reporting air temperature in the Arctic Ocean recently, but the measurements are not making it into the global synoptic weather reporting system – so I’m not sure whether they are being ingested into global weather /reanalysis models. The non-synoptic observations can be seen here (click the “Atmospheric Temperature” button):
          http://iabp.apl.washington.edu/maps_daily_map.html
          Click the the Arctic Table link on the upper left side of the map to see the data.

          • Looks like a polar classification for the mountains. And a rain forest near the equator not at much of an elevation. I am using the first above plot for the location. Very dry and cold, very wet and warm, lots of elevation change. Do you have something for precipitation or lack of it at BEST?

  10. • approach CERES net balanced Top-of-Atmosphere fluxes (EBAF-TOA product), where the global net is constrained to the ocean heat storage.

    Willis, since your last post I was trying to understand CERES and actually had their data quality summary open. I realise now that it’s probably a fools errand as I just don’t have the technical knowledge to penetrate the jargon.

    Anyhow, can somebody tell me what this means:

    Despite recent improvements in satellite instrument calibration and the algorithms used to determine SW and LW outgoing top-of-atmosphere (TOA) radiative fluxes, a sizeable imbalance persists in the average global net radiation at the TOA from CERES satellite observations. With the most recent CERES Edition4 Instrument calibration improvements, the SYN1deg_Edition4 net imbalance is ~4.3 W m-2, much larger than the expected observed ocean heating rate ~0.71 W m-2 (Johnson et al. 2016). This imbalance is problematic in applications that use Earth Radiation Budget (ERB) data for climate model evaluation, estimations of the Earth’s annual global mean energy budget, and studies that infer meridional heat transports. The CERES Energy Balanced and Filled (EBAF) dataset uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their ranges of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the Earth-atmosphere system.

    My poor brain can’t get past constructions like “expected observed” and it’s now convinced that there is something circular about data modelling modelled data!

    I’ve stopped listening to it though – my brain that is, at least until is smartens up a bit! 😉

    • Scott, the CERES instruments are more precise than they are accurate. Precision is how close repeated measurements of something are to each other. Accuracy is how close they are to reality.

      The CERES observations indicate a TOA imbalance of 4.3 W/m2 … but if that were the case, the difference would be visible in a few years. Since it isn’t the TOA imbalance must be much smaller. How big?

      They’ve set it to a value of 0.71 W/m2, based on the claimed rate of ocean heating … now me, I don’t think that we can measure the ocean that accurately. See my post entitled Decimals of Precision for some insight as to why I don’t think so.

      However, for most things that doesn’t matter. In general we’re looking at variations and at trends, and for those, precision is more important than accuracy.

      Hope this assists you,

      w.

      • Willis, thank you for responding. I’m giving up on CERES – too steep a learning curve – and have moved back to reading about the physiographically sensitive mapping of climatological temperature! I’m not joking, there are some great papers out there for a newb like me, that discuss spatial climate data sets in more prosaic language! 😉

        cheers,

        Scott

      • Willis,

        …the CERES instruments are more precise than they are accurate. Precision is how close repeated measurements of something are to each other. Accuracy is how close they are to reality.

        OK, fine. Please show how accurate the CERES instruments’ radiances (Level 1B data) are…references please, of course.

      • Repeatability, precision and accuracy are different attributes of an instrument. Another is the rate of drift, which sets the frequency for recalibration.

    • What it means is that the power metres on the satellite are calibrated using surface temperature
      measurements. Hence any measurement of the temperature from the CERES data ultimately relies
      on surface measurements and so it should not be seen as being an independent measurement in any
      sense.

      • Thanks, Percy. Think about that. IF the satellites were just slaves to the surface temperatures as you incorrectly claim … then why are the trends different?

        w.

  11. Willis, you note the greater land-based trends of Berkeley Earth and HadCRUT may well be due to encroaching civilization on measuring points. However, what’s the chance that “adjustments” (for whatever reasons) could play as big or bigger role in upwardly biasing their trends? We’ve seen a number of analyses that suggest temperature data set adjustments have a clear temporal upward bias…

  12. If you graph UAH to RSS up to 2015 before RSS was changed and warmed, the correlation was y = 0.9707x + 0.0964. Y is RSS, and X is UAH. R^2 is 0.9386, a very strong correlation. So RSS over the entire range is one-to-one compared to UAH and runs for that generation of model about 0.1C hotter. That is two-thirds most of the warming trend observed or claimed between two calculations by two models of the same thing, also called instrumentation/calculation deviation. So most claimed warming is within model variability and does not provably exist in the atmosphere of the earth as a real and observable entity. It is not what the warming is that matters. It is what the warming is above sample and instrumentation and model noise that matters, and is a very small number.

    • “So most claimed warming is within model variability and does not provably exist in the atmosphere of the earth as a real and observable entity. It is not what the warming is that matters. It is what the warming is above sample and instrumentation and model noise that matters, and is a very small number”
      …..and from this data we can prove the earth is experiencing CAGW. riiiight

  13. Well done for having the guts to admit that, if only all scientists were the same….

    However the shape of your CERES graph closely matched that of UAH, even if it was offset by .25 C, so your use of outgoing radiation to calculate temperature is valid.

    And the difference between oceans and land is still there to some degree in your new data.

    • Willis’ pay doesn’t depend on the results, his reputation does and unlike most that is important to him, so correcting errors is a matter of honor. As I have said before he is a treasure.

  14. As one of my colleagues has as his e-mail footer “”Pause for thought: If you’re not failing every now and again, it’s a sign you’re not doing anything very innovative.

  15. Willis, I am perplexed by this post..Why ?
    Well here in Australia my own intuition as a farmer since 1985, is that the climate in Victoria & SA is warming. And your first set of maps confirmed this intuition…Lots of area showing up red/orange..

    But the second set of globe maps shows far less warming in Australia and none of it in Vic & SA which is for counter intuitive…
    Ummmm ? Bugger ! A part of me is thinking “why did you correct that mistake 7 in public ?” And another aprt of me is thinking : “Wow, honesty is part of this blokes way of operating! ”

    But any thoughts on how the changes affect Australia ? I’m running a facebook group where we are discussing these issues.

    Cheers & thanks

    • Bill In Oz
      Out of interest how far down is the water table and is it the same as 1985? If it’s lower is that from extraction and has that increased? For glider pilots well drained farmland can be a great source of heat/lift, and in some cases matching anything from urbanization. Obviously this is still weather dependent.

      • An interesting question Stephen. In my own area over extraction over the past century has reduced the flow available from one bore; and in a second extraction has increased the salinity of watyer extracted from the aquifer…

        Curious we tend not to use the term “Ground water” in most of Australia. What with it being mostly dry and desert not much of the continent has any ground water as such.Instead what water there is lies at deepwe levels in aquifers.

        No gliders in my part of Oz…But then we lie beneath a major commercial flight path and also the flight path of helicopter ambulances & smaller commercial planes..

    • Another parallell effect: the same temperature feels much warmer at high humidity than at low because humans shed heat by evaporative cooling (=sweating). Has irrigation increased in the area? If so relative humidity has probably gone up and it would feel warmer.

      • No.

        However grape harvest time are now much earlier for the late ripening varieties..SA is a big wine area.So this is a pretty obvious response to warming at least in South Australia

  16. “So the main graphic in my last post is incorrect, with all of the trends being too low by about 0.24 °C/decade.”

    Is that a typo or am I missing something? The last post said warming at the rate of 0.07°C / decade, this post now shows the rate as 0.14°C / decade. That’s a difference of 0.07 not 0.24.

  17. “• The trend of the CERES temperatures is quite similar to the MSU UAH trend over both land and sea, and to the trend of the Reynolds OI sea surface temperature. I suspect that this indicates that the station-based land trends are gradually affected over time by encroaching civilization—more blacktop, more roads, more auto traffic, more jet exhaust and more powerful jet engines at the airport stations, more air conditioners, more sewage plants, more buildings, taller buildings, the list of things that can bias temperatures upwards is long.”

    I thought that once too a long time ago and so I compared the UAH maps with the berekeley earth maps.
    AND included data about land cover. Most people think the stations are all in urban areas but its actually not true. In any case. While the selection of a single emissivity is fine for trends, it assumes that there is no change in emissivity over time or seasonally. in the exploratory work I did it seemed like a good portion of the divergence between land (SAT) and the satellites were in areas where the emmissivity changes:
    ya, snow.

    At that point I went in search of satellite datasets that had snow cover maps over time but never got back to finishing the project.

    Short version. I tried to test whether those land grid cells that showed higher trends were IN FACT more urbanized or not.

    I’m sure you will recall that the original berkeley work on this used MODIS with 500m grid cells.
    The world has come a long way since then and you can get 300m data and 30m data if you have the
    patience. Even time series ( Landsat AVHRR) that show the changes in land cover over time.

    whats the gridcell sizes on CERES?

    • Steven, have you looked into PRISM and its physiographically sensitive mapping of climatological data? I came across them reading a brilliant paper from the Dutch about these issues.

      cheers,

      Scott

      • worked with PRISM years ago.
        if this is the PRISM u refer to
        http://static.berkeleyearth.org/posters/agu-2013-poster-1.pdf

        Bascially PRISM has a good regression for catching temperature inversions.
        Since our model says T = F(Lat, Elv) Inversions will cause errors in our model

        So, I wanted to see how PRISM worked and come up with a way to improve our model to
        account for seasonal temperature inversions.

        no funding. got killed

        • Yes, and PRISM is an interpolation method using a combination of deterministic and probabilistic approaches that also takes ancillary data into account in the interpolation process.

          “The PRISM (Parameter-elevation Relationships on Independent Slopes Model) interpolation method was used to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States.”

          “PRISM is a knowledge-based system that uses point measurements of precipitation, temperature, and other climate elements to produce continuous, digital coverages. PRISM incorporates expert knowledge of rain shadows, temperature inversions, coastal effects, and more.”

          http://prism.oregonstate.edu

          “The PRISM Group (formerly SCAS) was established at Oregon State University (OSU). Applications of PRISM Group products are wide-ranging, the PRISM Group is responsible for nearly all major climate mapping efforts at the federal level in the United States. It is also engaged in international modelling and analysis projects. PRISM showed to be very powerful in areas where the station network is unrepresentative for the variation in topography.”

  18. “The only thing I hate worse than being wrong is staying wrong”. That’s gotten me right more than my peers on a whole lot of issues.

  19. “…affected over time by encroaching civilization—more blacktop, more roads, more auto traffic, more jet exhaust and more powerful jet engines at the airport stations, more air conditioners, more sewage plants, more buildings, taller buildings, the list of things that can bias temperatures upwards is long.”

    Please Willis can we stop feeding the distorted narrative that jet aviation is a bigger problem than it is, or that it is a problem. This only helps give ammunition to such self righteous groups such as ‘plane stupid’. There are about 25,000 airliners in the world while there are 1,200,000,000 road vehicles. In comparison that is 0,000,025,000 airliners. All those road vehicles require roads but apart from airport runways there are no roads for aircraft. London Heathrow used to have 6 runways and it now has 2 and continues to move more passengers with less aircraft.
    https://upload.wikimedia.org/wikipedia/en/a/a5/Heathrow_Passenger_Statistic_Graph.jpg

    If we needed to warm the planet up we wouldn’t have a chance running all the jet engines even including all the military aircraft. If jet engines are so good at warming up the planet then why do airports have snow ploughs? I have searched through pictures of airports in snow and I don’t see the evidence that jet engines make even a dent in the snow cover apart from the odd photo of localized jet blasted snow from a stationary jet.
    However, great post, thank you.

    • Sorry Willis. I dived right in there and my mistake was being triggered by the mention of jet engines. I totally get your point that this is about the things that distort temperature measurements.

    • These jet airliners put copious amounts of water vapor into the 30,000′ to 40,000′ altitude of the atmosphere. A lot of the water condense into clouds which reflect IR up and down. I would suggest it isn’t running them on the ground that causes their impact, it is running them at altitude.

      Look up what impact grounding airline traffic across the US right after 9/11 did to the temperatures and clarity of the atmosphere.

      • rbabcock
        I cannot see how 30,000 odd aircraft, even if they were all flying at once, can compete with the volume of water vapor coming off the worlds oceans. The creation of condensation trails behind a jet engine is more to do the moisture that is already in the air, that is why contrails do not form all the time.
        Anyway, we are going off topic.

        • Does ANY water vapour coming off the worlds oceans manage to get up to 30,000 feet? It’s normally pretty dry up there.

  20. Looking at all those Land based graphs it is not easy to see the trend as the scales are +/- 1 degree.
    But it does look as if without the 2016 El Nino the trend would be much nearer Zero than 0.15/decade to 0.25/decade depending on recordset.
    The difference in dataset trends is quite important as it means between 1.5 and 2.5 degrees of warming over a century should the trend continue.
    Which I doubt very much.

  21. I have great faith in you Willis, but I found myself doubting your other post. I’m glad to see my faith is still justified.

  22. Good job. Science is all about the “truth” of a matter. And, being wrong in public is part of the deal.

    Occasionally, I physically shudder when I remember a couple of errors I published decades ago (smile). But, I’ve always said and felt ” All I care about is the truth. Even if it means I’m wrong”.

  23. Though skewed sinusoidal data-sets are indeed more than technically erroneous, the mistake is honest; the remedy is important; above all, the writer’s commitment to integrity is key.

    Would that your typical grunt-stuff climate deviant had one scintilla of this ability: “Heard melodies are sweet, but those unheard are sweeter; therefore, ye soft pipes, play on” (Keats).

  24. Willis …. the important part is, .. you are letting the data lead you to a conclusion, as opposed to trying to make the data go where you want it to go. Your error was an innocent error ….. the errors of the CAGW cabal are intentional, leading to false perceptions.

    • RE: the errors of the CAGW cabal are intentional, leading to false perceptions.
      But also leading to a steady pay check.

    • On thing that can greatly help is instead of using the rms fit using nonparametric Theil-Siegel fit. Its sensitivity to outliers is small. The break down point of the median is 50% instead of 0% for the rms fit. It is easy to program and probably is available in many stat software packages.

  25. Willis:

    You are wasting your amazing programming and analytical skills on “how many angels can dance on the head of a pin”. Your analyses are interesting, but do nothing to explain the “why” of climate change.

    As I have repeatedly pointed out, climate change has two components:

    1. Earth’s natural recovery from the Little Ice Age cooling, which was ~.05 deg. C. per decade, from 1900 to ~ 1970. (After then, it increased to ~0.16 deg. C. per decade, because of global clean air efforts that reduced dimming anthropogenic SO2 aerosol emissions, causing increased surface warming).

    2. All of the peaks and valleys on your graphs are simply the climatic response to varying amounts of SO2 aerosols in the atmosphere, of either volcanic (primarily), or anthropogenic origin.

    We have no control over the volcanic emissions, but, because of the Megatons of global reductions in anthropogenic SO2 aerosol emissions since circa 1975, mankind has been responsible for the gradual increase in average anomalous global temperatures–an unfortunate side effect of global Clean Air efforts!

    And the calculated amount of warming due to the reduction in SO2 aerosol emissions (~.02 deg. C. of temperature change for each net amount of change in global SO2 aerosol emissions) so precisely matches actual NASA (GISS) average global anomalous temperature values that there is simply no room for any additional warming due to “greenhouse gasses”

    Unless I have made a serious mistake in my analysis, it is all a hoax.

    • I don’t understand why you would assert:

      “mankind has been responsible for the gradual increase in average anomalous global temperatures–an unfortunate side effect of global Clean Air efforts!”

      Why is this unfortunate? Cold kills in numbers far exceeding warm, and we have evidence of past societies farming land more North and at higher altitudes. That would be a great benefit.

      We have evidence of trees being uncovered by receding ice sheets. Doesn’t warmer weather result in slower winds, less high temps, warmer low temps, and an overall more comfortable environment over the global average?

      • Matthew Drobnick:

        I totally agree with you that a moderate warming trend can be beneficial.

        What is “unfortunate” is that Clean Air efforts have the side effect of causing more surface warming, and that this warming has been mistakenly attributed to rising CO2 levels. This has led the waste of trillions of dollars in trying to control a harmless atmospheric gas, untold misery to millions of people because of higher energy costs, higher temperatures, and weather-related disasters.

        What is also unfortunate is that continuing efforts to reduce atmospheric SO2 levels via the reduction in the burning of fossil fuels will GUARANTEE that temperatures will continue to rise

    • “Unless I have made a serious mistake in my analysis”

      Yes, you did. You overestimate the SO2 effect and the impact of clean air act which btw is not being enforced in many countries and you do not take into account industrialization China, India and Brazil that certainly offset deindustrialization of Eastern Europe and Russia.

      • Unka:

        No, I have NOT overestimated the SO2 effect (Google my analysis in ”
        Climate Change Deciphered).

        With respect to China, in 2014 new regulations required that SO2 emissions be reduced, and between 2014 and 2016, they fell by ~29 Megatons. This massive reduction in global SO2 aerosol emissions was responsible for the 2014-16 “El Nino” warming (which ended because of the cooling from the VEI4 Wolf and Calbuco eruptions in early 2015).

        Google “Climate Sciences: India Surpassing China’s Sulfur Dioxide Emissions” for satellite-confirmed data on China’s reduction in SO2 emissions.

        • What counts is AOD and not some claims about Megatons.

          Here are AOD trends for China.

          “There were notable long-term annual trends in AOD in different regions over North China during 2001–2016: a decreasing AOD trend was found in Qinghai Tibet (−0.015 ± 0.010/decade), Northwest China (−0.059 ± 0.013/decade at 99% confidence level), and the North China Plain (−0.007 ± 0.021/decade), but a positive increasing trend was identified in northern Xinjiang (0.01 ± 0.006/decade), southern Xinjiang (0.002 ± 0.013/decade), East China (0.053 ± 0.042/decade), and Northeast China (0.016 ± 0.029/decade).”

          Peng Wang et al., Trends and Variability in Aerosol Optical Depth over North China from MODIS C6 Aerosol Products during 2001–2016, Atmosphere 2017, 8, 223; doi:10.3390/atmos8110223

          • Unka:

            “What counts is AOD and not some claims about Megatons”

            No, what counts is the global amount (Megatons) of dimming SO2 aerosol emissions in the atmosphere. If they decrease, average anomalous global land-ocean surface temperatures will increase, because there are fewer of them to reflect the sun’s rays away from the Earth’s surface.

            One might expect that there are also local effects (such as variations in AOD), helping to create “weather”.

  26. Well, everybody’s wrong.

    The fundamental problem with the RGHE theory is the popular assumption that space is cold. Just ask around, conduct a little survey, poll the “experts.”

    1) Space is cold
    a. The atmosphere acts as a thermal blanket as on a bed making the underside warm compared to the outer side cool.
    i. Due to surface BB upwelling, GHG “trapping” and downwelling LWIR & S-B – demonstrated by experiment as not possible.
    b. Due to PV=nRT – more nonsense.
    c. Per Q = U A dT – demonstrated daily by the insulated walls of a house.

    2) Space is hot
    a. 1,368 W/m^2, 394 K, 121 C, 250 F as actually experienced on the International Space Station and lunar surface.
    b. The atmosphere acts as a reflective shield similar to one placed behind a car’s windshield reflecting energy away and reducing the temperature inside the car, i.e. cooling.

    The atmosphere cools the earth by reflecting away 30% of the ISR therefore the atmosphere cannot warm the surface and RGHE does not exist.

    • Nick: Your assertion that space is hot is interesting. Temperature is proportional to the internal energy of matter (kinetic motion plus the energy in rotational, vibrational and electronic states). Technically speaking, temperature doesn’t exist in the absence of matter.

      When we say that space is cold or hot, we are talking about the temperature of an object in equilibrium with the local radiation field, which you correctly note is 1368 W/m2 from the sun and others correctly note is the equivalent of 3 K (5*10-6 W/m2) in cosmic microwave background. If you look up from the Earth and think in terms of solid angles, you see in one solid angle about 0.5 degrees by 0.5 degrees the sun shining down with 1368 W/m2 (about 960 after albedo) and 5*10-6 W/m2 from the remaining 359.5 by 359.5 degrees empty space. On the average, space is really cold.

      This way of looking at our sky is inaccurate because visible light is scattered in our atmosphere, making the sky look very bright blue in all directions. This is most apparent at twilight, when the sun is below the horizon but the sky is still blue. Nevertheless, this scattered light still originates from a the sun which occupies only a tiny fraction of the sky.

      The atmosphere does not heat the surface of the Earth in a thermodynamic sense – the net radiative flux is from the warmer surface to the colder atmosphere.

      Radiative fluxes in the atmosphere are calculated by means of the Schwarzschild equation, which is derived from the fundamentals of quantum mechanics. (See reference 10 in this article, written by a prominent skeptic.) Observations agree with these calculation. Either DLR exists or quantum mechanics is wrong. The 2LoT doesn’t apply to individual molecules and photons (QM does); the 2LoT is a consequence of large number so molecules following the laws of QM.

      https://en.wikipedia.org/wiki/Schwarzschild%27s_equation_for_radiative_transfer

  27. Willis: “There are two kinds of sailors: those who have run aground and those who lie they’ve never run aground.” Now we know which kind of sailor you are.

    • Man, don’t even say that, it reminds me of running aground on a reef at night in the Philippines one time … not a good party.

      w.

  28. Questions to author:

    (1) Could you write about how you calculate the trend?
    (2) Did you calculate the trend for, say, Berkeley or Reynolds data using your algorithm or just taking their number?
    (3) Is surface long wave upwelling corrected for absorption by the atmosphere?
    (4) You could tune emissivity value using some other set of data. You could get separate emissivities for land and sea or even seasonally adjusted.

    • Good questions, Unka.

      1) I did not write about it. What I do is take a sinusoidal dataset, and I calculate 12 trends, each one starting on one of the twelve months, using equal-length data. Then I average them.

      2) I calculated them myself.

      3) The surface upwelling LW is a calculated value, adjusted for everything relevant.

      4) I could tune the emissivity, but data to do it is scarce, and it makes very little difference to the results.

      w.

  29. “I suspect that this indicates that the station-based land trends are gradually affected over time by encroaching civilization—more blacktop, more roads, more auto traffic, more jet exhaust and more powerful jet engines at the airport stations, more air conditioners, more sewage plants, more buildings, taller buildings, THE LIST OF THINGS THAT CAN BIAS TEMPERATURES UPWARDS IS LONG.” (Emphasis added.)

    Amen and Alleluia!

    Putting it another way…If an observation site is chosen correctly at the onset, there is little that can change with or around the observation site to bias the temperature downward. Nearly all changes to the immediate environment will bias the temperature upwards! That is because a proper setting for an observation site is in an open, grass-covered field with no obstructions to the outgoing, longwave radiation. That’s one reason so many observation sites are at airports. Not only do pilots need the weather information at those points, but both the pilots and the observation sites require no vertical obstructions. Any growth, man-made or natural, will bias the temperature upward, and growth has been the rule around observation sites for over 100 years, not the exception.

    There have been studies on the poorly-named urban heat island effect (UHI), generally based on population density, which is readily available, but a poor proxy. Population does not have to change much or at all for man-made and natural growth to occur around an observation site.

    While there are very few things that can happen around an observation site to bias the temperature downwards, and all kinds of ways the temperature can be biased upwards, the gate-keepers of this surface data seem to have no ability to recognize the huge warming bias, and have no problem coming up with reasons to correct non-existent cooling biases. There is a ‘correction’ for the UHI effect, but it is woefully inadequate.

    For the life of me, I cannot understand how this manipulation of the data has not been widely exposed and prosecuted as scientific fraud! I guess it’s not a crime if the sheriff likes what you are doing.

    • At the beginning of the Industrial Revolution there was 1 city of a million people. Today there are over 500 cities of a million or more people. That is 500 more large sources of urban heat.

    • Actually look at Willis’ chart for where the Trends in Berkeley Earth are greater than CERES

      Willis says this

      “Where is the difference in trends between CERES and Berkeley Earth? As you might expect, it’s generally where there are fewer surface temperature stations—the upper Amazon, central Africa, the Arctic …”

      First what we see is a difference in TREND, not a difference in absolute T measured. Lets take the amazon.

      The Stations are measuring a higher trend. Is that because they are measuring UHI at 2meters?
      Well UHI at 2 meters is driven by SHUI, the surface UHI. And since its a trend, that would require increasing urbanization over that time period in that area. Remember it’s TREND that willis is computing. And a difference in trend under the UHI hypothesis would imply a difference in urbanization.

      The next step is simple.

      Identify those grid cells with the highest difference in trend and then investigate.

      A) for the surface record what stations compose the grid estimate.
      B) Have those grid cells ACTUALLY Increased in urbanization from 2000 to 2018

      Then with those same grid cells ( say the amazon area) investigate what else may have changed in the land cover.

      Let me put this analytically.

      At any given point on the globe the temperature is driven by a few key variables. In berkeley earth our formulation was this:

      T = F(L,E) where L= latitude and E is elevation. In this model the residual of the fit ( about 10% of unexplained T ) is called the weather. But, in reality its more than the weather. SOME of it is weather, and some of it is those variables that are hard to measure. For example, land cover.

      T = F(L,E,C) would be a better model where the final temperature is a function of Latitude, Elevation
      and Land cover. For example, Land cover— Like Urban land cover– is going to give you hotter
      T than grassland. And Bare earth is going to give you urban like temperatures.

      Now, the reason why we dont use Land cover in our model is that we dont have the information going back to 1750. We have modern day land cover, but only a little data on land cover back to 1750.
      So changes in land cover under our regression our going to be embedded in the residual, that is, they will show up as trend changes in the weather.

      In willis’ model of temperature he assumes a constant emissivity, that is, a constant land cover class.

      Lets take a simple example: Broadband epsilon for leafy areas ( LAI >2) is around .96 to .98
      Cut those trees down and change it to bare soil or desert? Desert is .9 . To make matters
      more complicated the moisture content in the soil is going to change the emissivity in
      certain wavelengths.

      The next step would be to…. LOOK. Typically what I do in these situations is I order the grid cells according to the greatest difference in trend. Then I plow into the data grid cell by grid cell.
      It’s painful slow work. Nothing publishable, yet. just years of failed efforts looking for something
      that will explain the trend.

      What am I looking for? Simple, I am looking for changes in land cover: rural to urban, forest to farmland, forested to bare dirt. Why? because if I cant find a change in land cover then that adds weight to the claim that the trend is a real change in the weather (residual), ie a change in climate.

  30. Willis: You are attempting to measure changes in surface temperature from the radiances that reach satellites in space. This is an extremely complicated process that involves models about how the atmosphere modifies surface radiances on their way to space, which means they must involve a model for the composition and temperature of the atmosphere in all grid cells at all times. That means the process is somewhat similar to that performed for temperature re-analysis (which also takes into account the flux that reaches satellites). I know there are significant problems with the thermometers we use to directly measure temperature at the surface, but the process of analyzing that data isn’t as prone to systematic error as the process used with satellite radiances.

    Systematic errors also plague measurement of sea level and bulk tropospheric temperature from space. The last set of corrections implemented by UAH were derived from radiosonde data, suggesting that any biases in the latter are likely to be in the former.

    • Willis’ conclusions were about relative data, trends with time that are self-referenced.

      In this case systematic error comes out in the wash.

      Thus Willis’ conclusions about warming oceans and cooling land are probably correct.

      The point of Willis’ work with CERES is to translate climagisterium Latin into English and give people direct access to important climate data.

      • I would not be so certain.

        Willis has a model for transforming flux into temperature that assumes a UNIFORM emissivity
        over space and a constant emmissivity over time, same as UAH assumes

        But we know emissivity is not uniform in space and time

        https://journals.ametsoc.org/doi/full/10.1175/JCLI3720.1

        His model agrees with the in situ observations over the ocean, lending credence to the model
        and the assumptions. It’s safe to say the ocean has a constant and uniform emissivity. So, his
        model assumption seem plausible and his results match observations. Ocean is warming over
        2000 to 2018. Pause? hmm.

        Over land his model disagrees with the in situ observations. which to believe?

        Do we reject his model? what would feynman say?

        Well, we know that model testing is not as simple as Feynman and Popper suggest. we dont simply reject Willis’ model of temperature because it disagrees with observations on the ground.

        We push harder, are the observations correct? are the assumptions of unchanging emissivity
        correct? And we also note that the observations are not totally free of modelling assumptions.
        Yes observations also rely on models and theory. When we compare willis’ satellite model
        of surface temps with surface temps we are really comparing them to a statistical model of surface temps. We compare two models. Always.

        There is no fast and easy way to do this. There is no simple case of theory over here and observation over there, as all observation is entangled with theory. A brilliant example
        of this is the first measurement of the speed of light.

        The observations of jupiters moon suggested a flaw in netwons model of gravity. Until the observations were adjusted to account for the speed of light. That is the RAW observation
        of the orbit of the moons assumed a world in which light travelled at an infinite speed.
        This assumption turned out to be false and so the raw measurement had to be adjusted to
        keep newtons theory intact. The adjustment factor was an assumed speed of light.

        Finding a difference between two temperature products ( all of which have modelling assumptions embedded) is the Beginning of the work. Not the end.

  31. Berkeley Earth: 0.27 °C/dec

    HadCRUT: 0.29 °C/dec

    UAH MSU: 0.17 °C/dec

    Converted Ceres: 0.16 °C/dec

    And here are the trends of the ocean data:

    Reynolds OI: 0.12 °C/dec

    Converted Ceres: 0.14 °C/dec

    UAH MSU: 0.11 °C/dec[you left out the “/”, but I put it in there]

    ……………… all UNDER 1 °C/dec

    Forgive me for asking, but is all your worry about being “wrong” in this small range really an exercise in quality control for its own sake [which is an admirable work ethic], rather than an outcome that has any relevance to reality ? Again, LESS THAN 1 °C/dec. Can we really know such things down to 1 °C/dec … in the actual world?

  32. It would be interesting to compare your Ocean record to the Pause buster Karl record for SST.

    Similar period, and it looks like you show more warming than the standard SST products.

    It would be interesting to show that your Ceres method vindicates the trends in the pause buster data.
    It appears to.

    One last thing to check.

    1. I believe your CERES record is measuring flux on a single pass per day.
    2. Note that you agree with SST which is also a single measurement
    3. You mismatch with a land record which is an average of Tmax and Tmin.

    next step: compare CERES land trend with

    A) trend in TMAX for berkeley ( adjusted and raw)
    B) trend in Tmin for berkeley ( Adjusted and raw)

    Overall, the trends in Tmax and tmin are roughly equal over this time period, but you would want to do it
    on a grid scale basis. If there are changes in urbanization on a grid scale basis, then you might
    expect this to show up in elevated Tmin.

  33. Willis,
    I don’t LIKE being wrong.
    I LIKE learning.
    So, I like finding out I was wrong because then I learn something.
    Sometimes, I learn I was wrong twice.
    Still learning.
    I can never be right all the time. I an never know everything. I want to be corrected when I get it ‘partial’ or wrong. That’s what friends do.
    I cannot live my life without opinions and thoughts.
    It’s simple. And complicated.
    God Bless You.

  34. Willis
    I posted this answer to ur questions on your previous post, but it did not show up there.

    http://oi60.tinypic.com/2d7ja79.jpg

    my data source is always http://www.tutiempo.net (historica)

    but if you already know that I looked at the data from 1942 then you might be aware of the original source.
    Remember my method: daily data is computed to give me yearly data which is summarized over periods (usually decades) by doing various backward regressions, giving me the derivatives of the least square equations, giving me the speed of warming/cooling in K/annum.
    Click on my name to read my final report as I do explain there the way I work.

    True enough, in the case of Elmendorf I did not have the whole cycle. At the time when I did this investigation, 2013, I found this report:
    http://iie.fing.edu.uy/simsee/biblioteca/CICLO_SOLAR_PeristykhDamon03-Gleissbergin14C.pdf

    Consequently
    I estimated the wavelength as being 88 years. Subsequent investigations, e.g. here:

    http://www.nonlin-processes-geophys.net/17/585/2010/npg-17-585-2010.html

    and also the measurements (going back to 1971) of the north south magnetic field strengths on the sun, lead me to believe that currently the cycle is 86.5 years. From 1971-2014 you can see exactly one half the GB cycle, namely, instead of drawing straight lines,

    http://oi63.tinypic.com/2ef6xvo.jpg

    you can imagine drawing bi-nomials from the top to the bottom to the top that represent the average field strengths with the dead end stops both in 1971 and 2014

    Assuming that maxima is a good proxy for incoming energy, that means that the sine wave of incoming energy was at its lowest point in 2014 and not in 2016 as I originally thought it was in 2013.

    Interesting is that Leif Svalgaard now also seems to support an 87 year cycle.

    More investigations/papers can be found in tables II and III, here,

    http://virtualacademia.com/pdf/cli267_293.pdf

  35. Willis:
    Some comments on your comment about emissivities. According to one of my text books, (Metallurgical Engineering, Volume 1, Schuhmann, 1952) emissivities can be much lower. Shiny metal things in particular. Highly polished aluminum for instance at 0.039-0.057. Interestingly, the emissivity of CO2 gas maxes out at about 0.18. Well. It never maxes out, but the increases are too small to be significant. It isn’t logarithmic. It is hyperbolic with an asymptote nearly parallel to the X axis.
    Curves for these are reproduced here. Top graph is CO2, bottom is H2O. Yes these curves come from solutions of the RTEs.
    https://johneggert.files.wordpress.com/2014/08/emissivities.jpg

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