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.
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.
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”.
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).
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.
Willis, some years ago I did a post on anomaly regression which shows how to handle anomaly regression properly. The solution handles the difficulty of calculating trends of cyclic data without worrying about the starting point and of comparing anomalized sets to raw data sets without the need for anomalizing first.
https://statpad.wordpress.com/2010/03/18/anomaly-regression-%e2%80%93-do-it-right/
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.
still in my bag o tricks
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”.
Burl Henry
“…an unfortunate side effect of global Clean Air efforts!”
Who says it’s unfortunate?
Stephen Skinner
“who says it’s unfortunate”
See my earlier 10:13 am post.
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.
Space is cold (3 K). The sun is hot (6000 K).
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
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.
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.
Not so fast
https://journals.ametsoc.org/doi/full/10.1175/JCLI3720.1
Spent a few years looking at this stuff, mostly the MODIS emissivity
Thanks, Mosh. The MODIS stuff is a third of a gigabyte PER MONTH … yikes. I’m looking around to see what else I can find that’s a bit more low-res.
w.
Yes I wasted months
“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.
never mind the mistake,
as soon as we are all clear that it is globally cooling:
on earth
I am not sure if John Tillman caught my last comment there?
https://wattsupwiththat.com/2018/12/05/cooling-down-the-land/#comment-2547944
Mouse fur.
Does that mean BALB/c or a C57BL/6
https://goo.gl/images/YNDuy2
?
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.
……………… 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?
While I don’t like being wrong, I like to be at least the second one to know about it.
As long as you don’t claim your error is “Settled Science”, you’re OK in my book.
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.
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.
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
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.
I’m confused. To save the Earth, do we need to eliminate all mice, or cover the Earth with mouse fur?
Bummer.
but you recovered well.
Thanks again.