Climate Research in the IPCC Wonderland: What Are We Really Measuring and Why Are We Wasting All That Money?

Climate Research in the IPCC Wonderland: What Are We Really Measuring and Why Are We Wasting All That Money?

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Guest Opinion: Dr. Tim Ball

A fascinating 2006 paper by Essex, McKitrick, and Andresen asked, “Does a Global Temperature Exist.” Their introduction sets the scene,

It arises from projecting a sampling of the fluctuating temperature field of the Earth onto a single number (e.g. [3], [4]) at discrete monthly or annual intervals. Proponents claim that this statistic represents a measurement of the annual global temperature to an accuracy of ±0.05 ◦C (see [5]). Moreover, they presume that small changes in it, up or down, have direct and unequivocal physical meaning.

The word “sampling” is important because, statistically, a sample has to be representative of a population. There is no way that a sampling of the “fluctuating temperature field of the Earth,” is possible. This problem of sample size is central to so much of what has gone wrong with climatology since the modelers took over. An early example was the 30-year normal. The original purpose was to provide a sample climate period for people like me reconstructing historical weather and climate record to use for comparisons. Gradually, it was adopted and adapted as the base for daily comparisons. We were told that it was the warmest or coldest day on record when they were only using the most recent 30-year normal. 30 years was chosen because in general statistics it is a valid sample size for a population. That has no relevance to weather and climate of the 4.5 billion years of the Earth’s existence. Part of the proof of this is that the 30-year normal period continually changes. We were told this was done because of more and better records. The reality is we have fewer stations now than in 1960 as NASA GISS explain (Figure 1a, # of stations and 1b, Coverage).

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Not only that, but the accuracy is terrible. US stations are supposedly the best in the world but as Anthony Watt’s project showed, only 7.9% of them achieve better than a 1°C accuracy. Look at the quote above. It says the temperature statistic is accurate to ±0.05°C. In fact, for most of the 406 years when instrumental measures of temperature were available (1612), they were incapable of yielding measurements better than 0.5°C.

The coverage numbers (1b) are meaningless because there are only weather stations for about 15% of the Earth’s surface. There are virtually no stations for

  • 70% of the world that is oceans,
  • 20% of the land surface that are mountains,
  • 20% of the land surface that is forest,
  • 19% of the land surface that is desert and,
  • 19% of the land surface that is grassland.

The result is we have inadequate measures in terms of the equipment and how it fits the historic record, combined with a wholly inadequate spatial sample. The inadequacies are acknowledged by the creation of the claim by NASA GISS and all promoters of anthropogenic global warming (AGW) that a station is representative of a 1200 km radius region. I plotted an illustrative example on a map of North America (Figure 2).

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Figure 2

Notice that the claim for the station in eastern North America includes the subarctic climate of southern James Bay and the subtropical climate of the Carolinas.

However, it doesn’t end there because this is only a meaningless temperature measured in a Stevenson Screen between 1.25 m and 2 m above the surface. Figure 3 shows the average range of daily temperatures at different levels between 2.5 cm and 17m above the surface. The curve for 1.2 m is important because that includes where most plants and animals exist a region known as the biosphere. It is the zone most critical to agriculture, yet all they get is information above it.

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Figure 3: Source: Oliver and Hidore, After Geiger 1950.

The Stevenson Screen data is inadequate for any meaningful analysis or as the basis of a mathematical computer model in this one sliver of the atmosphere, but there is even less as you go down or up. The models create a surface grid that becomes cubes as you move up. The number of squares in the grid varies with the naïve belief that a smaller grid improves the models. It would if there was adequate data, but that doesn’t exist. The number of cubes is determined by the number of layers used. Again, theoretically, more layers would yield better results, but it doesn’t matter because there are virtually no spatial or temporal data.

These layers are a result of the laminar flow that occurs in gases and liquids. However, the problems become worse when that flow is disturbed, and it becomes turbulent flow. Essex and McKitrick provide the best understandable explanation of the problems this creates for climate theory and climate models in their 2002 book Taken By Storm (revised edition). Turbulence is the reason the Intergovernmental Panel on Climate Change (IPCC) Scientific Section of Third IPCC Assessment Report, (2001) titled “Predictability in a Chaotic System” are forced to say;

“In sum, a strategy must recognise what is possible. In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible” (My emphasis)

I know from four different areas of my career about the variability of the layers in the atmosphere and the oceans. The first involved detecting Soviet submarines in the North Atlantic. One of the techniques involved detecting engine and other sounds emanating from the submarine and transmitted through the water. You determine the layers in the water by dropping a thermometer down from the surface measuring water temperature with depth (a bathythermograph). It was remarkable how many layers existed and how they changed over time. I was also surprised by the number of inversion layers, that is an increase of temperature with depth. This work and computer models only consider the surface of the oceans. That all falls within the friction layer of the oceans, which extends down to approximately 1000 m.

Beyond that, the layers are more clearly defined because laminar flow dominates. However, even in these layers the height at which you sample can yield very different results because the circulation is completely different in space and time on a global scale. The deep ocean circulation below the friction layer has cold water descending at the Poles and ascending at the Equator. I understand that some of these circulations can take thousands of years to complete. This means you can have energy stored in the current that is not added back into the atmosphere for a long time; it belies the insanity of a 30-year normal.

The second experience involved flying long distances at lower altitudes, that is below 10,000 feet. In other words, we were always in the frictional layer of the atmosphere, which varies, but on average extends up to 1000 m. We took a page from Benjamin Franklin’s navigation book. He directed the US Postal Service ships to Europe to sail on the north side of the Gulf Stream and North Atlantic Drift going east and on the south side to avoid it going west. We flew what we called pressure pattern navigation, that is a similar pattern because the wind patterns cause the ocean currents (Gyre). Again, the level of optimum winds varied all the time, so you could not predetermine the level. Instead, we began with the forecast winds, which were invariably wrong, and found the optimum level once airborne.

Above the atmospheric friction layer, the temperature is mostly laminar, but even here differences within a layer are important. We saw this during the exploitation of the so-called ozone hole. The ozone layer spans from 10 to 17 km above the surface to 50km, which means it spans from the troposphere to the stratosphere, but only at the Equator because the Troposphere only extends to 10 km over the Poles in summer. This is why diagrams as in Figure 4 are inaccurate.

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Figure 4

The reports were measurements of ozone from a selected layer. While there was a depletion in that layer, amounts were increasing in other levels. It didn’t matter because there was no evidence of CFCs affecting the ozone at any level within the layer. Indeed, when the speculation began, there was little knowledge about the all-important polar stratospheric clouds or as they were then known Noctilucent clouds.

The third experience involved the use of a 1000 ft radio tower just outside Winnipeg. We installed basic weather stations every 200 feet up the tower and recorded in real time (this is important for most users of weather data and is rarely available). Again, the number of layers and their change over time was remarkable with inversions occurring more frequently than expected. Each station at each level recorded distinctly different measures, and all of them varied from the official weather station at Winnipeg airport about 10 km away.

The fourth involved measurements to determine the pattern of the urban heat island effect (UHIE) in Winnipeg. Centered in an isotropic plain, it was an ideal place for such studies. We measured the spatial pattern of the UHIE but also tried to determine its profile. We did this comparing our surface with the tower data but also with instruments on high buildings in the city. The major finding was that the bubble of the UHIE was low, on average 300 m, but more importantly that the bubble shifted with wind patterns. This meant that you could not automatically apply a UHIE correction factor to Winnipeg Airport data. The Effect existed consistently but only affected the airport data under certain wind conditions. We also found that there was a distinct circulation pattern within the UHIE bubble as air rose in the warmest area went to the top of the bubble and diverged out and down into the suburbs like a mini convective cell. This was important for patterns of pollutant transfer, which we also studied by measuring their levels across the entire city.

So far, I have talked about the inadequacy of the temperature measurements in light of the two- and three-dimensional complexities of the atmosphere and oceans. However, one source identifies the most important variables for the models used as the basis for energy and environmental policies across the world.

“Sophisticated models, like Coupled General Circulation Models, combine many processes to portray the entire climate system. The most important components of these models are the atmosphere (including air temperature, moisture and precipitation levels, and storms); the oceans (measurements such as ocean temperature, salinity levels, and circulation patterns); terrestrial processes (including carbon absorption, forests, and storage of soil moisture); and the cryosphere (both sea ice and glaciers on land). A successful climate model must not only accurately represent all of these individual components, but also show how they interact with each other.”

The last line is critical and yet impossible. The temperature data is the best we have, and yet it is completely inadequate in every way. Pick any of the variables listed, and you find there is virtually no data. The answer to the question, “what are we really measuring,” is virtually nothing, and what we measure is not relevant to anything related to the dynamics of the atmosphere or oceans.

The IPCC Assessment Report 5 says the following in the Summary for Policymakers of the Physical Science Report.

Observations of the climate system are based on direct measurements and remote sensing from satellites and other platforms. Global-scale observations from the instrumental era began in the mid-19th century for temperature and other variables, with more comprehensive and diverse sets of observations available for the period 1950 onwards. Paleoclimate reconstructions extend some records back hundreds to millions of years. Together, they provide a comprehensive view of the variability and long-term changes in the atmosphere, the ocean, the cryosphere, and the land surface.

The first and last sentences are false. There are very few direct measurements, and the ones that exist are only representative of an extremely short period of time in a very limited area that is not even representative of the small area in which it was taken. Satellite data only covers from at best 1970 onward and in most cases, including temperature, does not provide global coverage. Again, from AR5, the surface temperature record, which has the best of inadequate records yields these results.

· The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] °C3, over the period 1880 to 2012, when multiple independently produced datasets exist. The total increase between the average of the 1850–1900 period and the 2003–2012 period is 0.78 [0.72 to 0.85] °C, based on the single longest dataset available4 (see Figure SPM.1). {2.4}

So, the best they can produce is an increase of 0.85°C, over 132 years with an error range of 0.20 below the average and 0.21°C above the average. As I understand, this is an error range of ±24%. But, they already admitted the data is of little value until 1950, which is less than half (62 years) of the period of record. Apparently, this is why they can only claim a discernible human impact from CO2 after 1950.

The reality is weather forecasting has not improved despite all the satellite and computer models: witness the most recent failures with hurricanes Florence and Michael. The other reality is that climate forecasting has deteriorated. Think what could be done with the trillions of dollars spent on computer models, government weather agencies, useless research, and unnecessary energy and environment policies based on their failed work. Why didn’t they spend this money on creating power grids and buildings capable of withstanding hurricanes? Every time there is a hurricane or even a mid-latitude cyclone, the power grid fails. That is just one small example of what could and should be done instead of this delusion that we are measuring the right things and somehow, we will eventually be able to forecast the weather and climate.

Ironically, all of this goes on because fossil fuels have created wealth, security, and better quality of life in every aspect of life across the world. The insanity this creates is that the IPCC wants to punish those nations who pioneered this advancement in the human situation and improvement in the human condition and deny similar opportunities to other nations by denying them the use of fossil fuels. Of course, they do all this from their fossil fueled corporate palaces in cities protected from the realities of the real world like Geneva, Washington, New York, and London. When will somebody stop this madness?

Superforest,Climate Change

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