# The AMO and Temperature

My Swedish is poor. But the graph is clear. Spitsbergen temperatures are in synch with the AMO. Some translated text below graph. (Thanks)

Contributing to making this part of the report a worrying reading is that the natural climate variations are not included in the used climate models. This is depressing, as it is well known that the air temperature around Svalbard is clearly influenced by the AMO (Atlantic Multidecadal Oscillation), which is a natural and periodic variation. This impact is actually mentioned in the report, and this realization ought to have been exploited.

Both the AMO and the air temperature in Longyearbyen have largely covaried since 1898 (see figure). The natural climate variations are thus far from unimportant, and explain better than all CO2-controlled climate models what actually takes place here in Longyearbyen.

AMO is known from measurements since 1856, and geological surveys show that AMO…

View original post 79 more words

# How does temperature depend on CO2?

From Clive Best:

Posted on February 15, 2019 by

Robert Rhode has produced a very nice animation of global temperatures as a function of CO2 levels in the atmosphere. Of course it is designed for public relations purposes in order to show increasing CO2 causes warming.  He even uses absolute temperatures which are not even directly measured. Here is my version of how temperature anomalies depend on CO2.

After a rather uncertain temperature rise from pre-industrial (280ppm) temperatures, there is a long period with no net warming between CO2 levels of 300 to 340 ppm, corresponding to the  period ~1939 to ~1980. Warming then continued as expected but then began tailing off towards a logarithmic dependency on CO2.

Many people will often glibly inform you that the CO2 greenhouse effect produces logarithmic radiative forcing, and state that this can easily derived from simple physics. However, few can really explain to you why it should be logarithmic, and it turns out that there is no simple proof as to why it should be. The often quoted formula for radiative forcing:

$S = 5.35 \times \ln{\frac{C}{C0}}$

can be traced back to a paper from 1998 in GRL (Myhre et al)

This formula is in reality a fit to some rather complex line by line radiative transfer calculations by hundreds of vibrational excitation states of CO2 molecules for absorption and re-emission of infrared radiation .  I have perviously described my own calculation of this radiative transfer and how you can fit a logarithmic dependency to it. The physical reason why increasing CO2 apparently produces a logarithmic forcing is that the central lines rapidly get saturated way up into the stratosphere, the strongest of which can then even cause cooling of the surface. Overall net warming is mostly due to strengthening of the weaker peripheral excitation  levels of the 15 micron band.

The net effect produces an apparent ‘logarithmic’ dependency, that I also calculated, and which is very similar to that of Myhre et al. Notice also how 3/4 of the “greenhouse” effect from  CO2  kicks in from zero to 400ppm.

The effect of increasing CO2 is to raise the effective emission height for 15micron IR radiation photons. The atmosphere thins out with height according to barometric pressure, and eventually the air is so thin that IR photons escape directly to space, thereby releasing energy from the atmosphere. Some IR frequencies can escape directly to space from the surface (the IR window). Others escape from cloud tops or high altitude water vapour and ozone.

The loss of energy from the top of the atmosphere drives convection and evaporation which is the primary heat loss from the surface. This process also drives the temperature lapse rate in the troposphere without which there could be no greenhouse effect. The overall energy balance between incoming solar insolation and the radiative losses to space determines the height of the tropopause and the earth’s  average temperature. A small sudden increase in CO2 will slightly reduce the outgoing radiative loss to space, thereby  creating an energy imbalance. This small energy imbalance is called “radiative forcing”. The surface will consequently warm slightly to compensate, thereby restoring the earth’s  energy balance.

This effect can be estimate from Stefan Boltzman’s law.

$S = \sigma \epsilon T^4$

$DS = 4 \sigma \epsilon T^3 DT$

If you assume T is constant (the answer increases by 1% for 1C if you don’t) then

$DT = \frac{DS}{4 \sigma \epsilon T^3}$

so with T = 288K and $\epsilon \approx 0.6$ and an effective insolation area of the earth of $\pi \times R^2$ this then  gives

$DT \approx 1.6 \times \ln{\frac{C}{C0}} (^\circ C )$

A steeper slope would be expected with net positive feedbacks

Figure 2 shows HadCRUT4.6 and my version of GHCNV3/HadSST3 plotted versus CO2 and compared to a logarithmic Temperature Dependence.

There is still a discrepancy in trends before CO2 reaches ~340ppm but thereafter temperatures follow a logarithmic increase with a scale factor of about 2.5. This implies a climate sensitivity (TCR) of about 1.7C .

# Met Office Decadal Forecasts Running Hot

By Paul Homewood

https://tallbloke.wordpress.com/2019/02/05/met-office-update-there-is-no-update/

Time to take a closer look at the new Met Office decadal forecast of global temperatures. (By decadal, the Met Office mean five years, apparently!)

Tallbloke handily updated the Met Office forecast from January 2017 with actual temperatures since (see above chart), in order to see how good their forecasting prowess actually was.

As you can see, it was pretty crap in reality!

For the period 2017-21, they predicted an anomaly range between 0.42 and 0.89C.

By stark contrast, the actual anomaly last year was 0.30C, way below the predictions.

It is also worth highlighting that even the retrospective predictions (that is retrospectively modelling past temperatures using known variables) were at the high end of the bands till the mid 1990s, and since have been trundling along the bottom with the exception of the record El Nino of 2015/16.

It is even more noticeable that the…

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# John Christy: Guilty as Charged (DeSmogBlog’s own goal)

Reblogged from Watts Up With That and Master Resource:

By Robert Bradley Jr. — February 5, 2019

John R. Christy is a professor of Atmospheric Science and Director of the Earth System Science Center at the University of Alabama in Huntsville. He’s a vocal critic of climate change models and has testified on numerous occasions against the mainstream scientific views on man-made climate change. Christy has affiliations with a number of climate science-denying think tanks, including the Heartland Institute and the Cato Institute. And now Andrew Wheeler has appointed him to serve on the U.S. Environmental Protection Agency’s Science Advisory Board.

Professor Christy is an excellent choice for EPA’s Science Advisory Board. And if you doubt me, please read the quotations below that DeSmogBlog has put up on its website to purportedly discredit EPA Secretary Wheeler’s choice. Christy’s views are mainstream in the world that most of us live in.

February, 2016

“The real world is not going along with rapid warming. The models need to go back to the drawing board.”

June, 2015

“[W]e are not morally bad people for taking carbon and turning it into the energy that offers life to humanity in a world that would otherwise be brutal (think of life before modernity). On the contrary, we are good people for doing so.”

April, 2015

“Carbon dioxide makes things grow. The world used to have five times as much carbon dioxide as it does now. Plants love this stuff. It creates more food. CO2 is not the problem.… There is absolutely no question that carbon energy provides with longer and better lives. There is no question about that.”

August, 2013

“I was at the table with three Europeans, and we were having lunch. And they were talking about their role as lead authors. And they were talking about how they were trying to make the report so dramatic that the United States would just have to sign that Kyoto Protocol.”

February, 2013

“If you choose to make regulations about carbon dioxide, that’s OK.  You as a state can do that; you have a right to do it.  But it’s not going to do anything about the climate. And it’s going to cost, there’s no doubt about that.”

March, 2011

“…it is fairly well agreed that the surface temperature will rise about 1°C as a modest response to a doubling of atmospheric CO2 if the rest of the component processes of the climate system remain independent of this response.”

May, 2009

“As far as the [2003 American Geophysical Union statement], I thought that was a fine statement because it did not put forth a magnitude of the warming. We just said that human effects have a warming influence, and that’s certainly true. There was nothing about disaster or catastrophe. In fact, I was very upset about the latest AGU statement [in 2007]. It was about alarmist as you can get.”

February, 2009

“We utilize energy from carbon, not because we are bad people, but because it is the affordable foundation on which the profound improvements in our standard of living have been achieved – our progress in health and welfare.”

December, 2003

In a 2003 interview with the San Francisco Chronicle, Christy describes himself as  “a strong critic of scientists who make catastrophic predictions of huge increases in global temperatures and tremendous rises in sea levels.”

“It is scientifically inconceivable that after changing forests into cities, turning millions of acres into farmland, putting massive quantities of soot and dust into the atmosphere and sending quantities of greenhouse gases into the air, that the natural course of climate change hasn’t been increased in the past century.”

Conclusion

The above quotations are neither radical nor errant. They are middle-of-the-roadish. John Christy knows that the climate changes and humans have a warming impact (good news indeed). And yes, the climate models are overpredicting real-world warming, a divergence that is growing, not contracting, as his iconic graph shows.

If Professor Christy sounds like a rational scientist working in a very unsettled field, you are correct. No “pretense of knowledge” here. Compare him to the know-it-all alarmist climatologists such as Andrew Dessler at Texas A&M, whose challenge to Texas Gov. Abbott was critically reviewed last week at MasterResource.

In fact, Dr. Christy (neutral profile here) is a global lukewarmer swimming upstream in a sea of Malthusian snowflakes, defined as those who think that the natural climate is optimal and that change cannot be good. (As Professor Dessler states: “… when it comes to climate, change is bad.” [1])

A polite, learned scientist, John Christy has to be among the most likeable physical scientists you will meet. (He was a star at a Houston Forum Climate Summit back in 1999, another story.) May America get to know him better in his new role.

———-

[1] Dessler, Introduction to Modern Climate Change (Cambridge, UK: Cambridge University Press, 2nd ed., 2016),  p. 146 (his emphasis).

# Zeke’s Wonder Plot

Reblogged from Clive Best:

Zeke Hausfather who works for Carbon Brief and Berkeley Earth has produced a plot which shows almost perfect agreement between CMIP5 model projections and global temperature data. This is based on RCP4.5 models and a baseline of 1981-2010. First here is his original plot.

I have reproduced his plot and  essentially agree that it is correct. However, I also found some interesting quirks. Firstly here is my version of his plot where I have added the CMIP5 mean to compare with the new blended TOS/TAS mean. I have also included the latest HadCRUT4.6 annual values in purple.

The apples to apples comparison (model SSTs blended with model land 2m temperatures)  reduces the model mean by about 0.06C. Zeke has also smoothed out the temperature data by using a 12 month running average. This has the effect of exaggerating peak values as compared to using the annual averages. To see this simply compare HadCrut4 (annual) in purple with his Hadley/UEA.

So now what happens if you change RCP?

Here is the result for RCP2.6 which has less forcing that RCP4.5

The model spread and the mean have increased slightly. So the model mean and grey shading should also  slightly rise.

Next, does the normalisation (baseline) affect the result ?

Yes it does. Shown above is the result for a normalisation from 1961-1990. Firstly look how the lowest 2 model projections now drop further down while the data seemingly now lies below both the blended (thick black) and the original CMIP average (thin black). HadCRUT4 2016 is now below the blended value.

This improved model agreement has nothing to do with the data itself but instead is due to a reduction in warming predicted by the models. So what exactly is meant by ‘blending’?

Measurements of global average temperature anomalies use weather stations on land and sea surface temperatures (SST) over oceans. The land measurements are “surface air temperatures”(SAT)  defined as the temperature 2m above ground level. The CMIP5 simulations however used SAT everywhere. The blended model projections use simulated SAT over land and TOS (temperature at surface) over oceans. This reduces all model predictions slightly, thereby marginally improving agreement with data.  See also Climate-lab-book

The detailed blending calculations were done by Kevin Cowtan using a land mask and ice mask to define where TOS and SAT should be used in forming the global average. I downloaded his python scripts and checked all the algorithm, and they look good to me. His results are based on the RCP8.5 ensemble. These are the results I get using his Python code.

Agreement has definitely now improved between the data (Cowtan a& Way) and the models, but they are still running warmer from 1998 to 2014.

Here finally is my 1950-2050 overview, where the blended RCP4.5 result has been added.

Again the models mostly lie above the data after 1999.

This post is intended to demonstrate just how careful you must be when interpreting plots that seemingly demonstrate either full agreement of climate models with data, or else total disagreement.

In summary, Zeke Hausfather writing for Carbon Brief 1) used a clever choice of baseline, 2) of RCP for blended models and 3) by using a 12 month running average, was able to show an almost perfect agreement between data and models. His plot is 100% correct.  However exactly the same data plotted with a different baseline and using annual values (exactly like those in the models), instead of 12 monthly running averages shows instead that the models are still lying consistently above the data. I know which one I think best represents reality.

# Mathematical modeling illusions

Reblogged from Watts Up With That:

The global climate scare – and policies resulting from it – are based on models that do not work

Dr. Jay Lehr and Tom Harris

For the past three decades, human-caused global warming alarmists have tried to frighten the public with stories of doom and gloom. They tell us the end of the world as we know it is nigh because of carbon dioxide emitted into the air by burning fossil fuels.

They are exercising precisely what journalist H. L. Mencken described early in the last century: “The whole point of practical politics is to keep the populace alarmed (and hence clamorous to be lead to safety) by menacing it with an endless series of hobgoblins, all of them imaginary.”

The dangerous human-caused climate change scare may well be the best hobgoblin ever conceived. It has half the world clamoring to be led to safety from a threat for which there is not a shred of meaningful physical evidence that climate fluctuations and weather events we are experiencing today are different from, or worse than, what our near and distant ancestors had to deal with – or are human-caused.

Many of the statements issued to support these fear-mongering claims are presented in the U.S. Fourth National Climate Assessment, a 1,656-page report released in late November. But none of their claims have any basis in real world observations. All that supports them are mathematical equations presented as accurate, reliable models of Earth’s climate.

It is important to properly understand these models, since they are the only basis for the climate scare.

Before we construct buildings or airplanes, we make physical, small-scale models and test them against stresses and performance that will be required of them when they are actually built. When dealing with systems that are largely (or entirely) beyond our control – such as climate – we try to describe them with mathematical equations. By altering the values of the variables in these equations, we can see how the outcomes are affected. This is called sensitivity testing, the very best use of mathematical models.

However, today’s climate models account for only a handful of the hundreds of variables that are known to affect Earth’s climate, and many of the values inserted for the variables they do use are little more than guesses. Dr. Willie Soon of the Harvard-Smithsonian Astrophysics Laboratory lists the six most important variables in any climate model:

1) Sun-Earth orbital dynamics and their relative positions and motions with respect to other planets in the solar system;

2) Charged particles output from the Sun (solar wind) and modulation of the incoming cosmic rays from the galaxy at large;

3) How clouds influence climate, both blocking some incoming rays/heat and trapping some of the warmth;

4) Distribution of sunlight intercepted in the atmosphere and near the Earth’s surface;

5) The way in which the oceans and land masses store, affect and distribute incoming solar energy;

6) How the biosphere reacts to all these various climate drivers.

Soon concludes that, even if the equations to describe these interactive systems were known and properly included in computer models (they are not), it would still not be possible to compute future climate states in any meaningful way. This is because it would take longer for even the world’s most advanced super-computers to calculate future climate than it would take for the climate to unfold in the real world.

So we could compute the climate (or Earth’s multiple sub-climates) for 40 years from now, but it would take more than 40 years for the models to make that computation.

Although governments have funded more than one hundred efforts to model the climate for the better part of three decades, with the exception of one Russian model which was fully “tuned” to and accidentally matched observational data, not one accurately “predicted” (hindcasted) the known past. Their average prediction is now a full 1 degree F above what satellites and weather balloons actually measured.

In his February 2, 2016 testimony before the U.S. House of Representatives Committee on Science, Space & Technology, University of Alabama-Huntsville climatologist Dr. John Christy compared the results of atmospheric temperatures as depicted by the average of 102 climate models with observations from satellites and balloon measurements. He concluded: “These models failed at the simple test of telling us ‘what’ has already happened, and thus would not be in a position to give us a confident answer to ‘what’ may happen in the future and ‘why.’ As such, they would be of highly questionable value in determining policy that should depend on a very confident understanding of how the climate system works.”

Similarly, when Christopher Monckton tested the IPCC approach in a paper published by the Bulletin of the Chinese Academy of Sciences in 2015, he convincingly demonstrated that official predictions of global warming had been overstated threefold. (Monckton holds several awards for his climate work.)

The paper has been downloaded 12 times more often than any other paper in the entire 60-year archive of that distinguished journal. Monckton’s team of eminent climate scientists is now putting the final touches on a paper proving definitively that – instead of the officially-predicted 3.3 degrees Celsius (5.5 F) warming for every doubling of CO2 levels – there will be only 1.1 degrees C of warming. At a vital point in their calculations, climatologists had neglected to take account of the fact that the Sun is shining!

All problems can be viewed as having five stages: observation, modeling, prediction, verification and validation. Apollo team meteorologist Tom Wysmuller explains: “Verification involves seeing if predictions actually happen, and validation checks to see if the prediction is something other than random correlation. Recent CO2 rise correlating with industrial age warming is an example on point that came to mind.”

As Science and Environmental Policy Project president Ken Haapala notes, “the global climate models relied upon by the IPCC [the United Nations Intergovernmental Panel on Climate Change] and the USGCRP [United States Global Change Research Program] have not been verified and validated.”

An important reason to discount climate models is their lack of testing against historical data. If one enters the correct data for a 1920 Model A, automotive modeling software used to develop a 2020 Ferrari should predict the performance of a 1920 Model A with reasonable accuracy. And it will.

But no climate models relied on by the IPCC (or any other model, for that matter) has applied the initial conditions of 1900 and forecast the Dust Bowl of the 1930s – never mind an accurate prediction of the climate in 2000 or 2015. Given the complete lack of testable results, we must conclude that these models have more in common with the “Magic 8 Ball” game than with any scientifically based process.

While one of the most active areas for mathematical modeling is the stock market, no one has ever predicted it accurately. For many years, the Wall Street Journal chose five eminent economic analysts to select a stock they were sure would rise in the following month. The Journal then had a chimpanzee throw five darts at a wall covered with that day’s stock market results. A month later, they determined who preformed better at choosing winners: the analysts or the chimpanzee. The chimp usually won.

For these and other reasons, until recently, most people were never foolish enough to make decisions based on predictions derived from equations that supposedly describe how nature or the economy works.

Yet today’s computer modelers claim they can model the climate – which involves far more variables than the economy or stock market – and do so decades or even a century into the future. They then tell governments to make trillion-dollar policy decisions that will impact every aspect of our lives, based on the outputs of their models. Incredibly, the United Nations and governments around the world are complying with this demand. We are crazy to continue letting them get away with it.

Dr. Jay Lehr is the Science Director of The Heartland Institute which is based in Arlington Heights, Illinois.

Tom Harris is Executive Director of the Ottawa, Canada-based International Climate Science Coalition.

# Southern Hemisphere Part 2

Reblogged from Musings from the Chiefio:

[HiFast note:  Really long post by E.M..  His BLUF/Conclusion]:

### In Conclusion

When you go through the other stations, you find very similar things. In the far south, most start late in history, have sporadic data drop outs, and various issues with data quality and availability.

Realize you can not fix this. The history is done and set. We can’t go back and open the station earlier, nor send someone to gather the 2010 data nor fill in August 2015.

This is the foundation of the whole Global Warming narrative. Then a huge layer of statistical manipulation is layered over it to attempt to hide the data quality and quantity issues. Kriging, interpolation, homogenizing, “the reference station method” of making up a number based on a temperature up to 1200 km away. None of this can fix the real problems with the underlying data. They can only burry it under a layer of bafflegab.

These are the months of data, that is not a missing data flag, for each wmo number in the Antarctic region (country starting with a 7). Note that the very first one has 10 years of data, that’s all. 120 months. THE longest is 1356 or about 113 years, then the next is 1212 months, or 101 years. Long for a human lifetime, nearly nothing in geological time scales and climate cycles. Most of the rest are around one human lifetime or less.

This is a continuation of the investigation started in this posting:
https://chiefio.wordpress.com/2019/01/30/the-southern-ocean-hemisphere-problem/

I had started to make it a comment, but it got a bit big for that.

Here’s a Southern Ocean view of the world:

The Southern Ocean around Antarctica

From the “Oh Boy! New Toy!” department:

I get to use my shiny new MySQL database to look at some of the things related to this problem. For starters, let’s ask just what stations are south of -45 degrees. That is, what are the reporting stations in the south 1/2 of the Southern Hemisphere. First up, how many are there?

mysql> SELECT COUNT(country) FROM inventory WHERE latitude < -45 ;
+----------------+
| COUNT(country) |
+----------------+
|             66 |
+----------------+
1 row in set (0.02 sec)


If you drop it down to the bottom 1/3 of the hemisphere, things start to look bleak:

mysql> SELECT COUNT(country) FROM inventory WHERE latitude < -60;
+----------------+
| COUNT(country) |
+----------------+
|             39 |
+----------------+
1 row in set (0.02 sec)


All of 39 stations. I happen to know from prior investigations that many of them have very short records. Antarctic stations that were only staffed for a few years or decades. The GHCN ship locations have spotty reporting and are up north.

But let’s move back out to the 1/4 of the globe, 1/2 of the hemisphere, point again.

That’s a total of 66 over the entire length of the record. For about 1/4 of the planet. But then you must also allow for the fact that many of these are short lived or have sporadic reporting. The SHIPS records especially, are only reported when a ship was in the area and bothered to take a temperature. Each ship with a different instrument of unknown calibration.

### The Stations

So what are these stations? Let’s report them, then inspect the “country” field to see where they are. Note that the 1 in the first digit is Africa, so the first 4 in the report are “Africa”, but 2 of them are islands. That’s right, all of 4 stations cover that large arc of area around Africa in the bottom 1/4 of the globe. (roughly as marked by the green line of the Southern Ocean boundary on the image above). So the data are already very limited even before we start asking “How long and how full is the record?” for each station.

Note too that nothing shows for the expanse of the Pacific where it reaches the Southern Ocean. All those “South Pacific Islands” are up where it is warm and nearer the tropics.

We then get a decent block of Patagonia. 13 Argentine stations. 6 from Chile, though at least one of them is really in Antarctica, and a couple of islands owned by the UK. After that, it’s a list of Antarctic stations with variable staffing durations. Remember that it was very difficult to keep an Antarctic base fully staffed and operational over the winter in the early 1900s. New Zealand shows up with 4. UPDATE (for Billinoz): Yes, and even one from Australia on McQuarie Island.

mysql> SELECT country, name, latitude FROM inventory WHERE latitude <-45;
+---------+--------------------------------+----------+
| country | name                           | latitude |
+---------+--------------------------------+----------+
| 141     | MARION ISLAND                  | -46.8800 |
| 143     | CROZET                         | -46.4300 |
| 143     | PORT-AUX-FRAN                  | -49.3500 |
| 147     | SIGNY ISLAND                   | -60.7200 |
| 301     | PATAGONES, ARGENTINA           | -46.8000 |
| 301     | COMODORO RIVA                  | -45.7800 |
| 301     | SARMIENTO ARGENTINA            | -45.6000 |
| 301     | GOBERNADOR GR                  | -48.7800 |
| 301     | PUERTO DESEAD                  | -47.7300 |
| 301     | LAGO ARGENTIN                  | -50.3300 |
| 301     | SAN JULIAN AE                  | -49.3200 |
| 301     | SANTA CRUZ AE                  | -50.0200 |
| 301     | RIO GALLEGOS                   | -51.6200 |
| 301     | RIO GRANDE B.                  | -53.8000 |
| 301     | ANO NUEVO                      | -54.7000 |
| 301     | USHUAIA AERO                   | -54.8000 |
| 301     | BASE ESPERANZ                  | -63.4000 |
| 304     | PUERTO AYSEN                   | -45.4000 |
| 304     | BALMACEDA                      | -45.9200 |
| 304     | ISLOTES EVANG                  | -52.4000 |
| 304     | PUNTA ARENAS                   | -53.0000 |
| 304     | CENTRO MET.AN                  | -62.4200 |
| 304     | BASE ARTURO P                  | -62.5000 |
| 316     | STANLEY /UK/                   | -51.7000 |
| 316     | CAPE PEMBROKE,FALKLAND ISL     | -51.7000 |
| 317     | GRYTVIKEN,                     | -54.2700 |
| 501     | MACQUARIE ISL                  | -54.4800 |
| 507     | INVERCARGILL                   | -46.7000 |
| 507     | DUNEDIN AERODROME              | -45.9300 |
| 507     | DUNEDIN MUSSELBURGH NEW ZE     | -45.9000 |
| 507     | CAMPBELL ISLA                  | -52.5500 |
| 700     | PETREL                         | -63.5000 |
| 700     | S.A.N.A.E. ST                  | -70.3000 |
| 700     | NEUMAYER                       | -70.6700 |
| 700     | AMUNDSEN-SCOT                  | -90.0000 |
| 700     | HALLEY                         | -75.5000 |
| 700     | BASE BELGRANO                  | -77.8700 |
| 700     | BELGRANO                       | -77.9000 |
| 700     | BELLINGSHAUSE                  | -62.2000 |
| 700     | DEST. NAVAL DECEPCION SOUT     | -63.0000 |
| 700     | DECEPTION IS.    S ATLANTI     | -63.0000 |
| 700     | ADMIRALITY BAY                 | -62.1000 |
| 700     | CMS"VICE.DO.M                  | -64.2300 |
| 700     | TENIENTE MATIENZO (ANT SOUTH A | -64.9700 |
| 700     | BASE ALMIRANTE BROWN           | -64.8800 |
| 700     | DEST. NAVAL MELCHIOR           | -64.3000 |
| 700     | BERNADO O'HIGGINS              | -63.3200 |
| 700     | PALMER STATIO                  | -64.7700 |
| 700     | ROTHERA POINT                  | -67.5700 |
| 700     | FARADAY                        | -65.2500 |
| 700     | BASE SAN MART                  | -68.1300 |
| 700     | BYRD STATION                   | -80.0200 |
| 700     | NOVOLAZAREVSK                  | -70.7700 |
| 700     | SYOWA                          | -69.0000 |
| 700     | MOLODEZNAJA                    | -67.6700 |
| 700     | MIZUHO                         | -70.7000 |
| 700     | MAWSON                         | -67.6000 |
| 700     | DAVIS                          | -68.5800 |
| 700     | MIRNYJ                         | -66.5500 |
| 700     | VOSTOK                         | -78.4500 |
| 700     | LENINGRADSKAYA                 | -69.5000 |
| 700     | CASEY                          | -66.2800 |
| 700     | DUMONT D'URVI                  | -66.6700 |
| 700     | MCMURDO                        | -77.8500 |
| 700     | SCOTT BASE                     | -77.8500 |
| 701     | BASE ORCADAS                   | -60.7500 |
+---------+--------------------------------+----------+
66 rows in set (0.02 sec)


When you make it the bottom 1/3 of the hemisphere it is basically Antarctica. One island, a couple of “Bases” assigned to Chile and Argentina, but in Antarctica, and then other stations in the deep freeze.

https://en.wikipedia.org/wiki/Signy_Island

Signy Island is a small subantarctic island in the South Orkney Islands of Antarctica. It was named by the Norwegian whaler Petter Sørlle after his wife Signy Therese.

mysql> SELECT country, name, latitude FROM inventory WHERE latitude <-60;
+---------+--------------------------------+----------+
| country | name                           | latitude |
+---------+--------------------------------+----------+
| 147     | SIGNY ISLAND                   | -60.7200 |
| 301     | BASE ESPERANZ                  | -63.4000 |
| 304     | CENTRO MET.AN                  | -62.4200 |
| 304     | BASE ARTURO P                  | -62.5000 |
| 700     | PETREL                         | -63.5000 |
| 700     | S.A.N.A.E. ST                  | -70.3000 |
| 700     | NEUMAYER                       | -70.6700 |
| 700     | AMUNDSEN-SCOT                  | -90.0000 |
| 700     | HALLEY                         | -75.5000 |
| 700     | BASE BELGRANO                  | -77.8700 |
| 700     | BELGRANO                       | -77.9000 |
| 700     | BELLINGSHAUSE                  | -62.2000 |
| 700     | DEST. NAVAL DECEPCION SOUT     | -63.0000 |
| 700     | DECEPTION IS.    S ATLANTI     | -63.0000 |
| 700     | ADMIRALITY BAY                 | -62.1000 |
| 700     | CMS"VICE.DO.M                  | -64.2300 |
| 700     | TENIENTE MATIENZO (ANT SOUTH A | -64.9700 |
| 700     | BASE ALMIRANTE BROWN           | -64.8800 |
| 700     | DEST. NAVAL MELCHIOR           | -64.3000 |
| 700     | BERNADO O'HIGGINS              | -63.3200 |
| 700     | PALMER STATIO                  | -64.7700 |
| 700     | ROTHERA POINT                  | -67.5700 |
| 700     | FARADAY                        | -65.2500 |
| 700     | BASE SAN MART                  | -68.1300 |
| 700     | BYRD STATION                   | -80.0200 |
| 700     | NOVOLAZAREVSK                  | -70.7700 |
| 700     | SYOWA                          | -69.0000 |
| 700     | MOLODEZNAJA                    | -67.6700 |
| 700     | MIZUHO                         | -70.7000 |
| 700     | MAWSON                         | -67.6000 |
| 700     | DAVIS                          | -68.5800 |
| 700     | MIRNYJ                         | -66.5500 |
| 700     | VOSTOK                         | -78.4500 |
| 700     | LENINGRADSKAYA                 | -69.5000 |
| 700     | CASEY                          | -66.2800 |
| 700     | DUMONT D'URVI                  | -66.6700 |
| 700     | MCMURDO                        | -77.8500 |
| 700     | SCOTT BASE                     | -77.8500 |
| 701     | BASE ORCADAS                   | -60.7500 |
+---------+--------------------------------+----------+
39 rows in set (0.10 sec)


Just as a sidebar, let’s look at that Chilean base:

https://en.wikipedia.org/wiki/Captain_Arturo_Prat_Base

Captain Arturo Prat Base is a Chilean Antarctic research station located at Iquique Cove, Greenwich Island in the South Shetland Islands, Antarctica.

Opened February 6, 1947 by the First Chilean Antarctic Expedition, it is the oldest Chilean Antarctic station.
Until March 1, 2006, it was a base of the Chilean Navy, on which date it was handed over to the regional government of Magallanes and Antártica Chilena Region. Until February 2004, it had been a permanent base. Afterwards, it had served as a summer base for ionospheric and meteorologic research. There have been plans to reopen the station for permanent occupation starting March 2008. The base is named for Captain Arturo Prat, a Chilean naval officer.

Opened in 1947. So for this base, all history starts after World War II. Now for a lot of folks that will seem like ancient history; but in terms of climate it is just yesterday. That’s a pretty short record, especially when you consider there are known cyclical changes of about 60 years and 1500 years. You will not be sampling a steady state flat condition, but a small part of a cycle.

Then we note that it is the oldest Chilean base. Any other of their bases will have even less data.

Then we note that it has been Summer Only since 2004 “Until February 2004, it had been a permanent base. Afterwards, it had served as a summer base for ionospheric and meteorologic research.”

If it is starting to sound “spotty”, you are right.

Here’s an excerpt of a report finding the WMO station number:

mysql> SELECT name, country, wmo FROM inventory WHERE country=304;
+------------------------------+---------+-------+
| name                         | country | wmo   |
+------------------------------+---------+-------+
| ARICA                        | 304     | 85406 |
[...]
| BASE ARTURO P                | 304     | 89057 |
+------------------------------+---------+-------+
31 rows in set (0.02 sec)


So there is a total of 31 stations in Chile, and this one is 89057 (or 30489057 with country code attached). But just what data made it from the station into the GHCN? Does it start in 1947? Um, no. This is GHCN v3.3, the latest before the new revision to GHCN v4 (that I could not download due to the government shutdown when building the database). It starts this station in 1966. I’ve chopped out some of the report below, to highlight the missing data flagged by -99.99 in some years. Note that this is not all the data that is missing. There is a pattern where the Dec – Feb data seem most prone to being missed. Changing station staff in Summer? I’ve not flagged all of them.

mysql> SELECT year, month, deg_c from temps_data where wmo=89057;
+------+-------+--------+
| year | month | deg_c  |
+------+-------+--------+
| 1966 |  JAN  |   1.80 |
| 1966 |  FEB  |   0.40 |
| 1966 |  MAR  |  -0.20 |
| 1966 |  APR  |  -1.40 |
| 1966 |  MAY  |  -6.80 |
| 1966 | JUNE  |  -7.70 |
| 1966 | JULY  | -12.60 |
| 1966 |  AUG  |  -5.70 |
| 1966 | SEPT  |  -5.50 |
| 1966 |  OCT  |  -3.00 |
| 1966 |  NOV  |  -1.70 |
| 1966 |  DEC  |   0.30 |
[...]
| 1980 |  JAN  |   1.10 |
| 1980 |  FEB  |   1.40 |
| 1980 |  MAR  |  -0.80 |
| 1980 |  APR  |  -4.60 |
| 1980 |  MAY  |  -3.40 |
| 1980 | JUNE  |  -6.50 |
| 1980 | JULY  | -12.70 |
| 1980 |  AUG  | -11.10 |
| 1980 | SEPT  |  -8.30 |
| 1980 |  OCT  |  -3.30 |
| 1980 |  NOV  |  -2.90 |
| 1980 |  DEC  |   0.20 |
| 1981 |  JAN  |   0.60 |
| 1981 |  FEB  |   1.40 |
| 1981 |  MAR  |  -0.30 |
| 1981 |  APR  | -99.99 |
| 1981 |  MAY  |  -3.40 |
| 1981 | JUNE  |  -4.30 |
| 1981 | JULY  |  -4.60 |
| 1981 |  AUG  |  -9.70 |
| 1981 | SEPT  |  -5.10 |
| 1981 |  OCT  |  -4.60 |
| 1981 |  NOV  |  -1.50 |
| 1981 |  DEC  |   0.70 |
| 1982 |  JAN  | -99.99 |
[...]
| 1987 |  JAN  |   0.60 |
| 1987 |  FEB  |   1.20 |
| 1987 |  MAR  |   0.10 |
| 1987 |  APR  |  -3.00 |
| 1987 |  MAY  |  -5.10 |
| 1987 | JUNE  |  -7.30 |
| 1987 | JULY  | -13.60 |
| 1987 |  AUG  |  -5.70 |
| 1987 | SEPT  |  -7.10 |
| 1987 |  OCT  |  -2.40 |
| 1987 |  NOV  |  -1.30 |
| 1987 |  DEC  |   1.20 |
| 1988 |  JAN  |   1.40 |
| 1988 |  FEB  | -99.99 |
| 1988 |  MAR  |   0.10 |
| 1988 |  APR  |  -1.00 |
| 1988 |  MAY  | -99.99 |
| 1988 | JUNE  | -99.99 |
| 1988 | JULY  | -99.99 |
| 1988 |  AUG  | -99.99 |
| 1988 | SEPT  | -99.99 |
| 1988 |  OCT  | -99.99 |
| 1988 |  NOV  | -99.99 |
| 1988 |  DEC  | -99.99 |
| 1989 |  JAN  | -99.99 |
[...]
| 1991 |  JAN  |   1.80 |
| 1991 |  FEB  |   0.60 |
| 1991 |  MAR  |  -0.70 |
| 1991 |  APR  |  -2.50 |
| 1991 |  MAY  | -99.99 |
| 1991 | JUNE  |  -8.80 |
| 1991 | JULY  |  -7.00 |
| 1991 |  AUG  |  -7.40 |
| 1991 | SEPT  | -99.99 |
| 1991 |  OCT  |  -3.80 |
| 1991 |  NOV  |  -1.20 |
| 1991 |  DEC  | -99.99 |
| 1992 |  JAN  | -99.99 |
| 1992 |  FEB  | -99.99 |
| 1992 |  MAR  | -99.99 |
| 1992 |  APR  |  -1.40 |
| 1992 |  MAY  |  -7.50 |
| 1992 | JUNE  |  -8.40 |
| 1992 | JULY  |  -7.70 |
| 1992 |  AUG  |  -5.50 |
| 1992 | SEPT  |  -2.90 |
| 1992 |  OCT  |  -3.10 |
| 1992 |  NOV  |  -0.30 |
| 1992 |  DEC  |   1.80 |
[...]
| 1994 |  JAN  |   1.80 |
| 1994 |  FEB  |   1.40 |
| 1994 |  MAR  |   1.20 |
| 1994 |  APR  |  -1.20 |
| 1994 |  MAY  |  -3.50 |
| 1994 | JUNE  | -99.99 |
| 1994 | JULY  | -10.30 |
| 1994 |  AUG  |  -4.10 |
| 1994 | SEPT  |  -4.10 |
| 1994 |  OCT  |  -5.60 |
| 1994 |  NOV  |   0.20 |
| 1994 |  DEC  |   0.00 |
[...]
| 1996 |  JAN  |   1.80 |
| 1996 |  FEB  |   2.50 |
| 1996 |  MAR  |   1.40 |
| 1996 |  APR  |  -1.20 |
| 1996 |  MAY  |  -2.20 |
| 1996 | JUNE  |  -4.90 |
| 1996 | JULY  |  -3.70 |
| 1996 |  AUG  |  -4.30 |
| 1996 | SEPT  |  -2.70 |
| 1996 |  OCT  |  -1.80 |
| 1996 |  NOV  |  -0.50 |
| 1996 |  DEC  |   0.80 |
| 1997 |  JAN  |   2.40 |
| 1997 |  FEB  |   1.80 |
| 1997 |  MAR  |   1.40 |
| 1997 |  APR  |  -1.60 |
| 1997 |  MAY  |  -1.70 |
| 1997 | JUNE  |  -4.60 |
| 1997 | JULY  |  -6.60 |
| 1997 |  AUG  |  -5.90 |
| 1997 | SEPT  |  -7.00 |
| 1997 |  OCT  |  -3.10 |
| 1997 |  NOV  |  -2.10 |
| 1997 |  DEC  |   0.70 |
| 1998 |  JAN  |   2.40 |
| 1998 |  FEB  |   3.00 |
| 1998 |  MAR  |   0.00 |
| 1998 |  APR  |   1.10 |
| 1998 |  MAY  |  -1.20 |
| 1998 | JUNE  |  -1.40 |
| 1998 | JULY  |  -5.50 |
| 1998 |  AUG  |  -7.00 |
| 1998 | SEPT  |  -8.10 |
| 1998 |  OCT  | -99.99 |
| 1998 |  NOV  |  -0.40 |
| 1998 |  DEC  |   0.50 |
| 1999 |  JAN  |   2.30 |
| 1999 |  FEB  | -99.99 |
| 1999 |  MAR  |   1.60 |
| 1999 |  APR  |   1.30 |
| 1999 |  MAY  |  -0.60 |
| 1999 | JUNE  |  -3.70 |
| 1999 | JULY  |  -3.60 |
| 1999 |  AUG  |  -5.50 |
| 1999 | SEPT  |  -5.60 |
| 1999 |  OCT  |  -1.60 |
| 1999 |  NOV  |   0.10 |
| 1999 |  DEC  |   1.50 |
| 2000 |  JAN  |   2.10 |
| 2000 |  FEB  | -99.99 |
| 2000 |  MAR  |   1.30 |
| 2000 |  APR  |   0.20 |
| 2000 |  MAY  |  -1.50 |
| 2000 | JUNE  |  -2.10 |
| 2000 | JULY  |  -3.00 |
| 2000 |  AUG  |  -6.00 |
| 2000 | SEPT  |  -5.80 |
| 2000 |  OCT  |  -1.70 |
| 2000 |  NOV  |  -0.80 |
| 2000 |  DEC  | -99.99 |
|[...]
| 2003 |  JAN  |   1.90 |
| 2003 |  FEB  |   1.90 |
| 2003 |  MAR  |   0.10 |
| 2003 |  APR  |  -0.50 |
| 2003 |  MAY  |  -2.70 |
| 2003 | JUNE  |  -7.20 |
| 2003 | JULY  |  -5.50 |
| 2003 |  AUG  |  -2.10 |
| 2003 | SEPT  |  -3.20 |
| 2003 |  OCT  |  -2.50 |
| 2003 |  NOV  | -99.99 |
| 2003 |  DEC  |  -1.10 |
| 2010 |  JAN  | -99.99 |
| 2010 |  FEB  | -99.99 |
| 2010 |  MAR  | -99.99 |
| 2010 |  APR  | -99.99 |
| 2010 |  MAY  | -99.99 |
| 2010 | JUNE  | -99.99 |
| 2010 | JULY  | -99.99 |
| 2010 |  AUG  | -99.99 |
| 2010 | SEPT  | -99.99 |
| 2010 |  OCT  | -99.99 |
| 2010 |  NOV  |   0.40 |
| 2010 |  DEC  |  -0.10 |
| 2011 |  JAN  |   1.20 |
| 2011 |  FEB  |   2.40 |
| 2011 |  MAR  | -99.99 |
| 2011 |  APR  |  -4.20 |
| 2011 |  MAY  |  -2.90 |
| 2011 | JUNE  |  -6.70 |
| 2011 | JULY  |  -9.20 |
| 2011 |  AUG  |  -8.00 |
| 2011 | SEPT  |  -6.50 |
| 2011 |  OCT  |  -1.70 |
| 2011 |  NOV  | -99.99 |
| 2011 |  DEC  | -99.99 |
| 2012 |  JAN  |   1.90 |
| 2012 |  FEB  |   1.40 |
| 2012 |  MAR  |   1.00 |
| 2012 |  APR  |  -4.00 |
| 2012 |  MAY  |  -2.50 |
| 2012 | JUNE  |  -6.40 |
| 2012 | JULY  |  -4.70 |
| 2012 |  AUG  |  -4.40 |
| 2012 | SEPT  |  -5.20 |
| 2012 |  OCT  |  -3.10 |
| 2012 |  NOV  |  -1.50 |
| 2012 |  DEC  |   0.00 |
[...]
| 2015 |  JAN  |   1.10 |
| 2015 |  FEB  |   1.50 |
| 2015 |  MAR  |   0.80 |
| 2015 |  APR  |   0.00 |
| 2015 |  MAY  |  -2.30 |
| 2015 | JUNE  |  -6.90 |
| 2015 | JULY  |  -7.90 |
| 2015 |  AUG  | -99.99 |
| 2015 | SEPT  | -99.99 |
| 2015 |  OCT  | -99.99 |
| 2015 |  NOV  | -99.99 |
| 2015 |  DEC  | -99.99 |
+------+-------+--------+
528 rows in set (10.82 sec)

mysql>


Then it ends in 2015 with another string of missing data. South America has had a few financial / political eruptions over the years, and has had trouble funding everything their government, and people, want funded. I’d also note the non-trivial problem that most Antarctic bases have grown in size and in fuel burned over the decades. It doesn’t take much to warm the area around a small town when you are worried about 1/10 C variation.

Now here’s the \$Billion Dollar Question: That 1940s data has July and August temps in the -11 to -12 C range. More recent records show -7 -8 or -9 range. Is that 3 to 4 C (roughly) drop due to a normal cycle of the Polar See-saw? Due to growth of the station and siting issues? Due to a natural 60 years cycle? Due to a change in the Southern Ocean currents under lunar tidal changes (known to have major cycles over an 1800 year span and with 60 year cycles as well)? Due to changes in volcanic activity on the peninsula? (When it is known that the volcanoes are now a bit active under the ice). Did it in fact have a cold spike and reach the prior history in 2010 or 2015, but that was when the data has a dropout?

How do you attribute causality with that short and sparse a data set?

### In Conclusion

When you go through the other stations, you find very similar things. In the far south, most start late in history, have sporadic data drop outs, and various issues with data quality and availability.

Realize you can not fix this. The history is done and set. We can’t go back and open the station earlier, nor send someone to gather the 2010 data nor fill in August 2015.

This is the foundation of the whole Global Warming narrative. Then a huge layer of statistical manipulation is layered over it to attempt to hide the data quality and quantity issues. Kriging, interpolation, homogenizing, “the reference station method” of making up a number based on a temperature up to 1200 km away. None of this can fix the real problems with the underlying data. They can only burry it under a layer of bafflegab.

These are the months of data, that is not a missing data flag, for each wmo number in the Antarctic region (country starting with a 7). Note that the very first one has 10 years of data, that’s all. 120 months. THE longest is 1356 or about 113 years, then the next is 1212 months, or 101 years. Long for a human lifetime, nearly nothing in geological time scales and climate cycles. Most of the rest are around one human lifetime or less.

mysql> SELECT COUNT(deg_c), wmo FROM temps_data WHERE region=7 AND deg_c>-100 GROUP BY wmo;
+--------------+-------+
| COUNT(deg_c) | wmo   |
+--------------+-------+
|          120 | 88963 |
|         1356 | 88968 |
|          444 | 89001 |
|          420 | 89002 |
|          708 | 89009 |
|          720 | 89022 |
|          732 | 89034 |
|          576 | 89050 |
|          588 | 89053 |
|         1212 | 89055 |
|          312 | 89059 |
|          324 | 89061 |
|          732 | 89062 |
|          840 | 89063 |
|          456 | 89066 |
|          312 | 89125 |
|          660 | 89512 |
|          684 | 89532 |
|          468 | 89542 |
|          180 | 89544 |
|          744 | 89564 |
|          660 | 89571 |
|          720 | 89592 |
|          912 | 89606 |
|          708 | 89611 |
|          732 | 89642 |
|          528 | 89664 |
|          372 | 89665 |
+--------------+-------+
28 rows in set (10.54 sec)


That’s pretty slim pickings in the Southern Hemisphere for any actual data. Now remember that a few times I’ve talked about “SHIP data”. There are some data collected from places in the ocean where, were a ship passing by, it could take a measurement. So how much SHIP data is in the Southern Hemisphere?

mysql> SELECT name, country, latitude FROM inventory WHERE region=8;
+--------+---------+----------+
| name   | country | latitude |
+--------+---------+----------+
| SHIP A | 800     |  62.0000 |
| SHIP B | 800     |  56.5000 |
| SHIP C | 800     |  52.8000 |
| SHIP D | 800     |  44.0000 |
| SHIP E | 800     |  35.0000 |
| SHIP I | 800     |  59.0000 |
| SHIP J | 800     |  52.5000 |
| SHIP K | 800     |  45.0000 |
| SHIP L | 800     |  57.0000 |
| SHIP M | 800     |  66.0000 |
| SHIP N | 800     |  30.0000 |
| SHIP P | 800     |  50.0000 |
| SHIP R | 800     |  47.0000 |
| SHIP V | 800     |  34.0000 |
+--------+---------+----------+
14 rows in set (0.03 sec)


Oh… that would be “none”. All that water is the Big Empty, per GHCN version 3.

# The Southern Ocean / Hemisphere Problem

Reblogged from Musings from the Chiefio:

Not offering a lot of information here, just admiring the problem. I’ll add some station information a bit later (for where we have stations recording the temperature).

THE big problem can be summed up in one map. Folks almost never look at the globe this way, yet it is vitally important to do so. We like to look at maps with all the land on them where all the people live (and all their thermometers are located…) yet the reality is that 1/2 of the world is essentially devoid of thermometers. Here’s a Southern Ocean view of the world:

The Southern Ocean around Antarctica

Here you can clearly see the basic problem. There’s nearly nothing in the “temperate” band around the southern hemisphere of the globe.

Your choices are Antarctic research stations, that were very poorly represented until fairly recent history, and even that is not very great; and some bits of South Africa and Patagonia. Both fairly wild even in the 1800s, and with political instability even now. Then Australia, that is still mostly empty in the Out-Back (I’ve driven into it. Did not see another car for hours, then days…).

Then note that even Australia and South Africa are closer to the Equator than the Pole.

There are some ship data in the GHCN, but not a lot; especially not in the early years. Then there’s that whole “bucket vs plumbing” measurement issue, and the question of just how deep the water was measured. Surface temperature and 20 feet down are quite different, and with ships having screws dozens of feet tall, there’s a lot of mixing in some shipping lanes. So just what was measured, eh? The data are more “correction” and “adjustment” than actual data.

I’m going to count up the stations by latitude and see what the count is, but it isn’t many. Some New Zealand, a bit of Australia, some Patagonia (when they were not having wars and revolutions), and a few stranded research stations in Antarctica.

Season that with knowing there IS a “Polar See-Saw”:

https://en.wikipedia.org/wiki/Polar_see-saw

The polar see-saw (also: Bipolar seesaw) is the phenomenon that temperature changes in the northern and southern hemispheres may be out of phase. The hypothesis states that large changes, for example when the glaciers are intensely growing or depleting, in the formation of ocean bottom water in both poles take a long time to exert their effect in the other hemisphere. Estimates of the period of delay vary, one typical estimate is 1500 years. This is usually studied in the context of ice-cores taken from Antarctica and Greenland.

So what looks like a “trend” in one hemisphere may in fact just be “counter phase” to the other where you have no suitable data.

So what do you do when there just isn’t any data to make a decent baseline and you don’t know the phase of the polar see-saw?

# Global Mean Surface Temperature: Early 20th Century Warming Period – Models versus Models & Models versus Data

This is a long post: 3500+ words and 22 illustrations. Regardless, heretics of the church of human-induced global warming who frequent this blog should enjoy it.  Additionally, I’ve uncovered something about the climate models stored in the CMIP5 archive that I hadn’t heard mentioned or seen presented before.  It amazed even me, and I know how poorly these climate models perform.  It’s yet another level of inconsistency between models, and it’s something very basic. It should help put to rest the laughable argument that climate models are based on well-documented physical processes.

View original post 4,910 more words

# Climate Models Cover Up

Making Climate Models Look Good

Clive Best dove into climate models temperature projections and discovered how the data can be manipulated to make model projections look closer to measurements than they really are. His first post was A comparison of CMIP5 Climate Models with HadCRUT4.6 January 21, 2019. Excerpts in italics with my bolds.

Overview: Figure 1. shows a comparison of the latest HadCRUT4.6 temperatures with CMIP5 models for Representative Concentration Pathways (RCPs). The temperature data lies significantly below all RCPs, which themselves only diverge after ~2025.

Modern Climate models originate from Global Circulation models which are used for weather forecasting. These simulate the 3D hydrodynamic flow of the atmosphere and ocean on earth as it rotates daily on its tilted axis, and while orbiting the sun annually. The meridional flow of energy from the tropics to the poles generates convective cells, prevailing winds, ocean currents and weather systems. Energy…

View original post 969 more words