Ventura Photonics Climate Post 000.1 Sept. 7 2021

Roy Clark

Ventura Photonics (VP) has conducted research into sensor and remote sensing related topics including climate energy transfer and climate change since 2009. This has included the analysis of the time dependent energy transfer at the land-air and ocean-air interfaces, atmospheric radiative transfer calculations at high spectral and spatial resolution and the analysis of meteorological surface air temperature (MSAT) data from selected weather stations. This work has revealed serious flaws in the conventional description of the ‘greenhouse effect’ and the computer models used to simulate the earth’s climate. The fundamental error is the use of the equilibrium average climate assumption to simplify the climate energy transfer processes. This presumes that there is an exact flux balance between an average absorbed solar flux and the average emitted longwave IR (LWIR) flux at the top of the atmosphere. The result is an elegant set of equilibrium flux balance equations that have no relationship whatsoever to the earth’s climate. The so called radiative convective equilibrium models must create global warming as a mathematical artifact of the underlying assumptions. Physical reality has been abandoned in favor of mathematical simplicity. The equilibrium climate models are fraudulent, by definition, before any computer code is even written. There is no ‘24 hour average sun’ shining in the sky at night. The 2 C (or 1.5 C) temperature limit established by the Paris Climate Accord is based on nothing more than the pseudoscience of radiative forcing, feedbacks and a contrived climate sensitivity to CO2 in a fictional ‘equilibrium average’ climate. Irrational belief in computer climate model ‘prediction’ has replaced logic and reason. Eisenhower’s warning about the corruption of science by government funding has come true. There is no ‘climate emergency’.

The diurnal and seasonal time delays or phase shifts between the peak solar flux and the temperature response provide irrefutable evidence for a non-equilibrium climate. This is not a new discovery. The subsurface seasonal phase shift was described by Fourier in 1824 and 1827 [Fourier, 1824, 1827]. The invalid assumption of an equilibrium climate was introduced by Pouillet in 1836 over a decade later [Pouillet, 1836]. The use of this mathematical equilibrium construct creates a false sensitivity of the surface temperature to changes in LWIR flux produced for example by a change in the atmospheric CO2 concentration. By the 1960s, the equilibrium climate assumption had become accepted as scientific dogma and was incorporated into the early ‘radiative convective equilibrium’ climate models. Seven fundamental scientific errors were introduced in just two of the early climate modeling papers by Manabe and Wetheald [1967] and Hansen et al [1981]. These errors have never been corrected and provide the foundation for the massive, multi-trillion dollar fraud we have today. This fraud has three parts.

First, there is the scientific fraud. Predictions of the global warming apocalypse based on the mathematical artifacts created by the climate equilibrium and related assumptions rapidly became a lucrative source of research funds. The scientific method of iteration through discovery and hypothesis in climate science collapsed. The peer review process has been thoroughly corrupted. Blind advocacy has replaced scientific reason. Our so called ‘climate scientists’ have become prophets of the quasi-religious cult of the global warming apocalypse. Second, there is an institutional fraud. When government agencies such as NASA and the Atomic Energy Commission were created, there was no provision to shut them down or change them if their mission changed or ended. The climate fraud has now become a long term source of income for many so called government ‘scientists’. They have become trapped in a web of lies of their own making. They have chosen to live in an equilibrium climate fantasy world of forcings and feedbacks and climate sensitivity to CO2. Third, there was a deliberate decision by various environmental and political groups to exploit the climate fraud to promote their own non-scientific interests. For example, there was a 1975 conference ‘The Atmosphere Endangered and Endangering’ organized by Margaret Mead the anthropologist [Hecht, 2007]. Her objective was to exploit atmospheric pollution –real or imagined - for population control. This was based on exaggerated concerns over population growth promoted for example by Paul Ehrlich in his 1968 book ‘The Population Bomb’. Attendees included Stephen Schneider and John Holdren. Both were strongly influenced by Ehrlich. Schneider became a leading advocate of the CO2 climate scare at Stanford University. Holdren became science and technology advisor to President Obama [Hecht, 2007]. In the 1980s, politicians such as Margaret Thatcher and Al Gore used the climate fraud to promote their political careers. The United Nations Intergovernmental Panel on Climate Change (UN IPCC) was formed in 1988. It is a political body, not a scientific one. Its mission is to assess ‘the scientific, technical and socioeconomic information relevant for the understanding of the risk of human-induced climate change’. This is based on the a-priori assumption that human activities are causing CO2 induced global warming. There never was an attempt to objectively evaluate the scientific evidence of the cause of climate change. The only climate crisis that we have is a massive pyramid or Ponzi scheme that needs to be shut down.

The non-equilibrium description of climate energy transfer using time dependent flux terms has been discussed in prior publications by Roy Clark including the VP monograph ‘The Dynamic Greenhouse Effect and the Climate Averaging Paradox’ and the papers ‘Dynamic Thermal Reservoirs’ Parts I and II [Clark, 2011: 2013]. More recent research at Ventura Photonics has expanded on the concepts presented in these earlier papers. The results of this research are summarized in the rest of this post. Further details are provided in five additional Ventura Photonics Climate Posts:

1) VPVP 1: The Greenhouse Effect on a Rotating Water Planet.

This post explains the greenhouse effect in terms of the time dependent partial LWIR exchange energy at the surface.

Links: Greenhouse Effect Post, Greenhouse Effect .pdf

2) VPVP 2: Trapped in a Web of Lies of Their Own Making

This post examines the history of climate science and the development of the climate modeling fraud.

Links: Web of Lies Post, Web of Lies .pdf

3) VPVP 3: Surface Temperature and the Pseudoscientific 2 C Temperature Limit in the Paris Climate Accord

This post examines the surface temperature in terms of coupled thermal reservoirs. The various phase shifts, the convection transition temperature and the effects of ocean evaporation are included.

Links: Surface Temperature Post, Surface Temperature .pdf

4) VPVP 4: Forcing the Climate Fraud

This post explains the pseudoscience of radiative forcing, feedbacks and climate sensitivity to CO2.

Links: Forcing the Climate Fraud Post, Forcing the Climate Fraud .pdf

5) VPVP 5: The Seven Sins of the Climate Models

This post shows how the simplifying assumptions used in the early climate models introduces seven fundamental scientific errors in the climate models.

Links: Seven Sins Post, Seven Sins .pdf

Recent Research At Ventura Photonics

The basic concept is that a change in surface temperature under non-equilibrium energy transfer has to be calculated using the change in heat content or enthalpy of the surface reservoir, divided by the heat capacity. The change in heat content is determined by the interaction of four main time dependent flux terms with the surface reservoir. These are the absorbed solar flux, the net LWIR emission, the moist convection (evapotranspiration) and the subsurface thermal transport. These are interactive and should not be separated and analyzed independently of each other. The net LWIR flux is determined by the partial LWIR exchange energy between the upward LWIR flux emitted by the surface and the downward LWIR flux to the surface from the lower troposphere. This is real source of the so called ‘greenhouse effect’. The energy transfer processes at the land-air and ocean-air interfaces are different and have to be evaluated separately. The phase shift between the peak solar flux and the temperature response also has to be considered. These energy transfer processes are illustrated schematically in Figure 1.

Figure 1: a) Energy transfer at the land surface and b) at the ocean surface. c) The time delay or phase shift between the peak solar flux and the temperature response (schematic).

The ascending air produced by convection from the surface is a mass transport process that is coupled to both the gravitational potential and the rotation or angular momentum of the earth. These interactions produce our basic weather patterns including the Hadley, Ferell, polar convective cell structure, the trade winds and the ocean gyre circulation. The troposphere functions as an open cycle heat engine that transports part of the surface heat to higher altitudes by convection. Some of the heat is stored as gravitational potential energy. From here the heat is radiated back to space, mainly by the water bands.

At the top of the atmosphere (TOA), the LWIR flux returned to space is simply a cooling flux. Conservation of energy requires a planetary average LWIR flux near 240 W m-2. This is the cumulative LWIR emission from many different levels through the atmosphere. The emission from each level is modified by the absorption and emission of the levels above. The spectral distribution of the LWIR flux is not that of a blackbody near 255 K. The use of Stefan’s Law to create an ‘effective emission temperature’ is a mathematical construct with little physical meaning. There can be no ‘greenhouse effect temperature’ of 33 K.

There are three important non-equilibrium parameters that follow from the time dependent analysis of the surface temperature. These are the time delay or phase shift between the peak solar flux and the surface temperature response, the sensitivity of the ocean latent heat flux to the wind speed and the diurnal convection transition temperature. In addition, there is no requirement for an exact flux balance between the absorbed solar flux and the cooling flux at the ocean surface. This leads to natural quasi-periodic ocean oscillations. The ‘climate sensitivity’ to CO2 claimed by the equilibrium climate modelers is mainly the warming phase of the Atlantic Multi-decadal Oscillation (AMO) since 1980 coupled to the weather station data through the diurnal convective transition temperature. There is also a contribution from the ‘adjustments’ to the weather station data made as part of the climate averaging process.

The most obvious phase shift is the seasonal delay of 4 to 8 weeks at higher latitudes between the peak solar flux and the temperature response. This is illustrated in Figure 2 which shows the 30 year 1981 to 2010 daily climate averages for selected US weather stations near 45° N [WRCC, 2020]. The minimum and maximum temperatures and the seasonal phase shifts are given in Figures 2a, 2b and 2c. This type of data has been recorded as part of the weather station data for well over 100 years. From a scientific perspective the phase shift is sufficient evidence to invalidate the equilibrium climate model results. For an equilibrium climate there should be no phase shift. From a scientific perspective, the phase shift is sufficient evidence to invalidate the equilibrium climate model results. For an equilibrium climate there should be no phase shift. This is the case for the moon under solar illumination. This is discussed in mere detail in the ‘Greenhouse Effect’ post. Seasonal phase shifts are also be observed in ocean temperatures. These may now be obtained from Argo float data [Argo, 2020]. There are also diurnal phase shifts that may reach 2 hours or more after local noon. However, these have not generally been recorded a part of the weather station record. Phase shifts are considered in more detail in the companion Surface Temperature post.

Figure 2: 1981 to 2010 30 year daily average climate data for selected weather stations near 45° N. a) Minimum temperature, b) maximum temperature and c) phase shift.

Over the oceans, the penetration depth of the LWIR flux below the surface is 100 microns or less [Hale and Querry, 1973]. This means that the LWIR flux is fully coupled to the wind driven surface evaporation and cannot be separated and analyzed independently of the latent heat flux. Therefore an important parameter is the sensitivity of the latent heat flux to the wind speed. This may be estimated using the long term zonal averages of the latent heat flux and the wind speed from Yu et al [2008]. This is shown in Figure 3. Within the ±30° latitude bands, the latent heat flux sensitivity is at least 15 W m-2/m s-1. Since the start of the industrial revolution in about 1800, the atmospheric concentration of CO2 has increased by approximately 120 ppm from 280 to 400 ppm. This has produced an increase in downward LWIR flux from the lower troposphere to the surface near 2 W m-2 [Harde, 2017]. This is dissipated by an increase in wind speed of approximately 13 cm s-1. This is much smaller than the normal variation in wind speed, so there can be no measurable increase on ocean temperature from the 120 ppm increase in CO2 concentration. At present, the average increase in atmospheric CO2 concentration is 2.4 ppm per year. The increase corresponding increase in downward LWIR flux is near 0.034 W m-2. The sensitivity of the ocean evaporation to the wind speed is considered in more detail in the Surface Temperature post. This also means that the argument that an increase in the atmospheric CO2 concentration will produce an increase in ‘extreme weather events’ is fraudulent.

Figure 3: Sensitivity of the latent heat flux to the wind speed based on long term zonal averages from Yu et al [2008].

Over land, almost all of the absorbed solar flux is dissipated within the same diurnal cycle. As the surface warms during the day, the excess heat is removed by moist convection (evapotranspiration). Some of the heat is conducted below the surface, stored and returned to the surface later in the day. In the evening, the surface cools and the convection essentially stops as the surface and air temperatures equalize. The surface then cools more slowly over night by net LWIR emission. The equalization or convection transition temperature is reset each day by the local weather system passing through. Under certain weather conditions, such as those involving ‘blocking’ high pressure systems or downslope winds, there is a downward flow of dry air. This is heated by compression. The dry air adiabatic lapse rate is 9.8 K km-1. This can produce a temperature rise of 10 C or more over a period of a few days or less [Black et al, 2004. Math, 1934].

Many weather systems form over the oceans and then move overland. The bulk air temperature of the weather system is related to the ocean surface temperatures along the ocean path. This provides the coupling mechanism that links ocean surface temperatures to the land based weather station record. Over the oceans, there is no requirement for an exact flux balance between the solar heating and the wind driven evaporative cooling. This leads to characteristic quasi-periodic oscillations in ocean surface temperature. Short term oscillations with periods between 3 and 7 years include the El Nino Southern Oscillation (ENSO). Longer term oscillations with periods between 60 and 70 years include the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO). The results of the climate models are usually compared to the ‘global temperature anomaly’ such as the HadCRUT4 climate record. This is an area weighted average of long term weather station and ocean temperature data with the mean subtracted. However, the raw data have been adjusted or ‘homogenized’ to correct for various ‘biases’ and include the effects of urban heat islands and station changes [Andrews, 2017a 2017b 2017c D’Aleo, 2010]. There is also an underlying linear trend. This is the temperature recovery from the Maunder minimum or Little Ice Age (LIA) [Akasofu, 2010]. The coupling of the AMO to the HadCRUT4 climate record is illustrated in Figure 4. This shows the annual averages of the HadCRUT4 global temperature anomaly and the AMO from 1856 [HadCRUT, 2020. AMO, 2020]. Both the longer term 60 year maxima and minima and the shorter term ‘fingerprint’ detail of the AMO can clearly be seen in both plots. The correlation coefficient between the two data sets is 0.8. From 1970 onwards, the HadCRUT4 series is offset approximately 0.3 C higher than the AMO. This requires further investigation and is probably related to adjustments made to the HadCRUT4 data, urban heat island effects (UHI), and station changes. The linear slope of the AMO and a least squares sine wave fit to the oscillation are also shown. The oscillation period is 61 years and the amplitude is ±0.2 C. The fit is extended to 2050 to indicate the probable cooling trend in the AMO as it switches to its cooling phase.

Figure 4: The AMO and the HadCRUT4 annual average temperature anomaly from 1856. The linear slope and sine wave fit to the AMO are also shown.

The coupling of the AMO to the climate record through the diurnal convection transition temperature has been ignored by the equilibrium climate modelers. It has been assumed, incorrectly that all of the recent temperature increase found in the HadCRUT4 temperature anomaly can be attributed to the observed increase in atmospheric CO2 concentration. This has been used to create a fraudulent ‘climate sensitivity’ to CO2 as discussed in more detail below. The first person to attribute AMO warming to CO2 was Callendar in 1938. His analysis of weather station data included the warm phase of the AMO from 1910 to 1935 [Callendar, 1938]. The cooling phase of the AMO from 1940 to about 1970 was used to create the 1970s global cooling scare [McFarlane, 2018 Peterson et al, 2008 Douglas, 1975 Bryson and Dittberner, 1976]. Both Hansen et al [1981] and Jones et al [1986] chose to ignore the 1940 AMO peak and called it ‘noise’. The role of the AMO in the creation of global warming and cooling is illustrated in Figure 5. The publication by Jones et al [1986] and a similar conference proceeding by Wigley et al [1985] mark the start of the modern climate fraud based on the correlation between the AMO and the climate record.

Figure 5: a) AMO anomaly and HadCRUT4 global temperature anomaly, aligned from 1860 to 1970, from figure 4 b) temperature anomaly for N. temperate stations from Callendar [1938], c) temperature anomaly for global cooling from Douglas, 1975, d) global temperature anomaly from Hansen et al, [1981] and e) global temperature anomaly from Jones et al, [1986]. The changes in CO2 concentration are also shown in c and d [Keeling, 2020]. The periods of record for the weather station data are also indicated.

The earth is an isolated planet that is heated by electromagnetic radiation from the sun and cooled by the LWR flux emitted back to space. However, the earth is not in thermal equilibrium. The rate of heating does not equal the rate of cooling. The First Law of Thermodynamics, Conservation of Energy, only requires an approximate long term average planetary energy balance between the absorbed solar flux and the LWIR flux returned to space. Starting with the work of Pouillet in 1836, the climate energy transfer processes were oversimplified by using the equilibrium climate assumption. Speculation that changes in atmospheric CO2 concentration could cycle the earth through an Ice Age began with the work of Tyndall in the 1860’s [1861 1863]. The first crude calculations of a possible change in surface temperature produced by changes in the atmospheric CO2 concentration were made by Arrhenius in 1896. He was interested in explaining Ice Age cycles. He used the equilibrium climate assumption to estimate the effects of both an increase and a decrease in the CO2 concentration. He assumed that an average surface temperature was determined by an ‘equilibrium’ between the average absorbed solar flux and average LWIR flux. An increase in atmospheric CO2 concentration absorbed more of the LWIR flux which led to an increase in surface temperature. His results did not apply to planet earth. He neglected ocean evaporation and convection effects. However, this established the idea that changes in CO2 concentration could cause climate change and this was soon extended to include fossil fuel combustion as well as carbonate rock weathering effects. By the 1960’s, the concept of CO2 induced warming in an equilibrium climate had become accepted scientific dogma. This was incorporated into the first computer climate models starting with the work of Manabe and Wetherald (M&W) ‘Thermal equilibrium of the atmosphere with a given distribution of relative humidity’ [M&W 1967].

The simplifying assumptions used by M&W were clearly and honestly stated on the second page of their paper. They introduce four fundamental scientific errors:

1) There is no equilibrium climate state on any time or spatial scale.

2) There is no such entity as a blackbody surface with zero heat capacity.

3) The concept of a fixed relative humidity distribution is incorrect.

4) The upward and downward LWIR fluxes through the atmosphere are not equivalent. Instead, they are decoupled by molecular linewidth effects. This leads to the formation of two independent tropospheric thermal reservoirs.

The fourth error follows from molecular line broadening effects. This was not explicitly considered by M&W.

Later modeling ‘improvements’ introduced three more fundamental scientific errors. These ‘improvements’ were discussed by Hansen et al in their 1981 paper ‘Climate impact of increasing carbon dioxide’ [Hansen et al, 1981].

5) A ‘slab’ ocean model was used instead of the M&W blackbody surface.

6) A prescribed mathematical ritual of ‘radiative forcing’ was introduced.

7) There was a ‘bait and switch’ change from surface temperature to the weather station temperature record.

The only change to the basic modeling assumptions since 1981 was the addition of ‘efficacies’ to the radiative forcings by Hansen et al [2005]. These modeling errors are considered in more detail in the 'Seven Sins' post.

Geological studies of ocean sediments and related work demonstrated that the chronology of the Ice Ages was determined by planetary perturbations to the earth’s orbital and axial rotations known as Milankovitch cycles. In particular, the 100,000 year nominal Ice Age period was produced by changes to the eccentricity of the earth’s orbit [Hays et al, 1976]. A more detailed discussion was published in 1979 [Imbrie and Imbrie, 1979]. A review of CO2 induced warming effects derived from equilibrium climate models, including feedback effects, known as the Charney report was also published in 1979. At the time of this review, the results from only five GCMs were available, 3 from Manabe’s group and 2 from Hansen’s group. The reviewers concluded that a warming of 3±1.5 C from a ‘doubling’ of the atmospheric CO2 concentration was likely [Charney, 1979]. The mathematics used in the climate ‘models’ appeared reasonable based on the acceptance of the invalid equilibrium assumption, so no further investigation was needed. The publication of the book ‘Ice Ages’ by Imbrie and Imbrie and the Charney report may be used to establish 1979 as the time at which the use of equilibrium climate models had clearly transitioned from invalid scientific hypothesis to downright fraud.

The reviewers involved in the Charney report made no effort to validate the climate model results with any kind of thermal engineering calculations of the surface temperature. Nor did they understand that the satellite spectral data available at that time were clear evidence of non-equilibrium climate energy transfer [Hanel et al, 1971]. They also chose to ignore the history of CO2 induced climate change and its origin as the explanation as the cause of an Ice Age cycle. Since changes in the atmospheric CO2 concentration were no longer needed to explain the Ice Age cycles, there was no reason to expect that an increase in CO2 concentration from fossil fuel combustion would produce any climate warming. The reviewers also chose to ignore the fact that the climate models were based on weather forecasting algorithms that required the numerical solution to a very large number of coupled non-linear equations. That such calculations were unstable was demonstrated by Lorenz in 1963. Weather forecasts could only be made for up to 12 days in advance before these instabilities became problematic [Lorenz, 1963, 1973]. There was no reason to expect that the climate models had any predictive capabilities over the much longer time scales involved in climate change.

The work of Hansen et al [1981] established the foundation for the pseudoscience of radiative forcings, feedbacks and climate sensitivity to CO2 used in the equilibrium climate models today. It is assumed that there is an exact flux balance at the top of the atmosphere between the long term average of the absorbed solar flux and the LWIR flux returned to space. An increase in the atmospheric concentration of CO2 is then assumed to perturb this equilibrium climate state by absorbing a small fraction of the LWIR flux returned to space. The decrease in LWIR flux at TOA is called a radiative forcing. The climate system is then supposed to respond by ‘adjusting’ to a new equilibrium state with a higher surface temperature that restores the LWIR flux at TOA [Knutti and Hegerl, 2008]. An elaborate climate modeling ritual has been established in which a hypothetical doubling of the atmospheric CO2 concentration propagates through this fictional equilibrium climate [Hansen, 2005 IPCC, 2013, Chapter 8]. There are two pseudoscientific ‘climate sensitivities’. The first is an ‘equilibrium climate sensitivity’ (ECS) and the second is a ‘transient climate response’ (TCR). The ECS is the equilibrium climate temperature response to a radiative forcing after the model oceans have adjusted to a new equilibrium state and the TCR is the response to a gradual increase in the radiative forcing, usually from a 1% per year increase in CO2 concentration before equilibrium is reached [IPCC 2013 Chapter 9].

The fraudulent creation of the climate sensitivity from the global temperature anomaly record may be illustrated using the work of Otto et al [2013]. They define the climate sensitivities as:

The change in temperature is taken from the HadCRUT4 global temperature anomaly [HadCRUT4, 2020] and the radiative forcings are taken from the CMIP5 /RCP4.5 model ensemble. The change in heat content is dominated by ocean heat uptake. More recent estimates of ECS and TCR are provided by Lewis and Curry [2018]. The decadal temperature and forcing estimates from data given by Otto et al [2013] are shown in Figures 6a and 6b. The 1910 AMO cycle minimum and the 1970 maximum are indicated. The increase in the downward LWIR flux related to the ‘radiative forcing’ shown in Figure 6b cannot couple below the ocean surface and cause any measurable change in ocean temperature. To show the relationship to the AMO more clearly, the HadCRUT4 data used by Otto et al is overlapped with the AMO data from Figure 4 and plotted in Figure 6c. The influence of the AMO extends over large areas of North America, Europe and parts of Africa through the propagation of the ocean surface temperature by weather systems that are formed over the Atlantic Ocean. The ocean surface temperature is coupled to the weather stations through the convection transition temperature. Using tree ring analysis, the AMO has been reconstructed back to 1567 [Gray et al, 2004 Gray.NOAA, 2021]. This is shown in Figure 6d. None of the observed temperature changes associated with the AMO can be attributed to an increase in atmospheric CO2 concentration.

Figure 6: a) Decadal mean temperature estimates derived from the HadCRUT4 global mean temperature series b) decadal mean forcing with standard errors from the CMIP5 /RCP4.5 ensemble. Data from Otto et al [2013], c) Figure a) with the AMO plot overlay and d) AMO reconstruction from 1567.

Using the radiative forcing approach it is claimed that the ECS is in the range from 2.1 to 4.7 C based on the CMIP5 model ‘ensemble’. In the US, this modeling effort is coordinated by the climate group at Lawrence Livermore National Laboratories (LLNL). They also maintain the ‘library’ of climate model results [Stauffer et al, 2017, Taylor et al, 2012]. The CMIP5 model results were used by the UN Intergovernmental Panel on Climate Change (IPCC) in their fifth Climate Assessment Report (AR5) [IPCC 2013, Chap. 9]. For the upcoming AR6 IPCC report, the ECS of the CMIP6 climate model ‘ensemble’ is given as 1.8 to 5.6 K [Hausfather, 2019]. These climate sensitivities are shown in Figure 7. The median ECS of 3.8 C/280 ppm translates into a temperature sensitivity of about 74 ppm C-1. A 2 C temperature rise corresponds to a CO2 concentration of approximately 430 ppm. This is the pseudoscientific basis of the 2 C temperature limit incorporated into the Paris Climate Accord [Luning and Vahrenholt, 2017]. When the increase in downward LWIR flux is used to calculate the change in surface temperature using the time dependent flux terms, a ‘doubling’ of the CO2 concentration from 280 to 560 ppm produces an increase of 0.3 C in the minimum surface temperature. An increase in CO2 concentration of 8500 ppm corresponding to an increase in flux of 20 W m-2 only produces an increase in temperature of 1.4 C. This is discussed in more detail in the 'Surface Temperature' post.

Figure 7: Equilibrium climate sensitivity (ECS) for a doubling of the CO2 concentration from 280 to 560 ppm for selected CMIP5 and CMIP6 climate models.

It is time to shut down the climate modeling fraud and repair the economic damage. Those responsible should be held accountable.


This work was performed as independent research by the author. It was not supported by any grant awards and none of the work was conducted as a part of employment duties for any employer. The views expressed are those of the author.


Normally, the references given in an article of this nature would be almost exclusively to the peer reviewed literature, with limited references to websites that provide access to climate data. Unfortunately, climate science has been thoroughly corrupted by the global warming fraud. The peer review process has collapsed and been replaced by blatant cronyism. Many of the publications in ‘prestigious’ journals such as Nature, Science, PNAS and others that relate to climate modeling predictions of global warming are fraudulent and should never have been published. Consequently many of the important references given here are to website publications. This should not detract from the integrity of the information provided. Many of these website publications have received a more thorough review than they might have received through the traditional peer review process.

Akasofu, S-I, Natural Science 2(11) 1211-1224 (2010), ‘On the recovery from the Little Ice Age’ http://dx.doi.org/10.4236/ns.2010.211149 Akasofu

AMO2020, http://www.esrl.noaa.gov/psd/data/correlation/amon.us.long.data AMO

Andrews, R., 2017a, Energy Matters Sept 14, 2017, ‘Adjusting Measurements to Match the Models – Part 3: Lower Troposphere Satellite Temperatures’.

http://euanmearns.com/adjusting-measurements-to-match-the-models-part-3-lower-troposphere-satellite-temperatures/#more-19464 Andrews_a

Andrews, R., 2017b, Energy Matters Aug 2, 2017, ‘Making the Measurements Match the Models – Part 2: Sea Surface Temperatures’.

http://euanmearns.com/making-the-measurements-match-the-models-part-2-sea-surface-temperatures/ Andrews_b

Andrews, R., 2017c, Energy Matters July 27, 2017, ‘Adjusting Measurements to Match the Models – Part 1: Surface Air Temperatures’.

http://euanmearns.com/adjusting-measurements-to-match-the-models-part-1-surface-air-temperatures/ Andrews_c

Argo, 2020, https://argo.ucsd.edu/data/data-visualizations/marine-atlas/ Argo

Black, E., M. Blackburn, G. Harrison, B. Hoskins and J. Methven, Weather 59(8), pp. 217-223 (2004), ‘Factors contributing to the summer 2003 European heatwave’. https://doi.org/10.1256/wea.74.04 Black

Bryson, R. A. and G. J. Dittberner, J. Atmos. Sci. 33(11) 2094-2106 (1976), ‘A non-equilibrium model of hemispheric mean surface temperature’.

https://journals.ametsoc.org/view/journals/atsc/33/11/1520-0469_1976_033_2094_anemoh_2_0_co_2.xml Bryson

Callendar, G. S., J. Roy. Met. Soc. 64 223-240 (1938), ‘The artificial production of carbon dioxide and its influence on temperature’

https://doi.org/10.1002/qj.49706427503 Callendar_1

available at:

http://www.met.reading.ac.uk/~ed/callendar_1938.pdf Callendar_2

Charney, J. G. et al, Carbon Dioxide and Climate: A Scientific Assessment, Report of an ad hoc study group on carbon dioxide and climate, Woods Hole, MA July 23-27 (1979) https://www.bnl.gov/envsci/schwartz/charney_report1979.pdf Charney

Clark, R., 2013, Energy and Environment 24(3, 4) 319-340 (2013), ‘A dynamic coupled thermal reservoir approach to atmospheric energy transfer Part I: Concepts’.

https://doi.org/10.1260/0958-305X.24.3-4.319 Clark_a


Energy and Environment 24(3, 4) 341-359 (2013), ‘A dynamic coupled thermal reservoir approach to atmospheric energy transfer Part II: Applications’.

https://doi.org/10.1260/0958-305X.24.3-4.341 Clark_b

Clark, R., 2011, 'The dynamic greenhouse effect and the climate averaging paradox', Ventura Photonics Monograph', VPM 001, Thousand Oaks, CA, Amazon, 2011. http://www.amazon.com/Dynamic-Greenhouse-Climate-Averaging-Paradox/dp/1466359188/ref=sr_1_1?s=books&ie=UTF8&qid=1318571038&sr=1-1 Clark_c

D’Aleo, J. ‘Progressive Enhancement of Global Temperature Trends’, Science and Public Policy Institute, July 2010.

http://scienceandpublicpolicy.org/science-papers/originals/progressive-enhancement D'Aleo

Douglas, J. H., Science News 107 138-140 March 1, (1975), ‘Climate change: chilling possibilities’,

https://www.sciencenews.org/wp-content/uploads/2008/10/8983.pdf Douglas

Fourier, B. J. B. Mem. R. Sci. Inst., 7 527-604 (1827), ‘Memoire sur les temperatures du globe terrestre et des espaces planetaires’.

https://www.academie-sciences.fr/pdf/dossiers/Fourier/Fourier_pdf/Mem1827_p569_604.pdf Fourier_1827a

English translation:

https://courses.seas.harvard.edu/climate/eli/Courses/EPS281r/Sources/Greenhouse-effect/more/Fourier-1827.pdf Fourier_1827b

Fourier, B. J. B., Annales de Chimie et de Physique, 27, pp. 136–167 (1824), ‘Remarques générales sur les températures du globe terrestre et des espaces planétaires’. https://gallica.bnf.fr/ark:/12148/bpt6k65708960/f142.image# Fourier_1824a

English translation:

http://fourier1824.geologist-1011.mobi/ Fourier_1824b

Gray, S. T. L. J. Graumlich, J. L. Betancourt and G. T. Pederson, Geophys. Res. Letts, 31 L12205, pp1-4 (2004) doi:10.1029/2004GL019932, ‘A tree-ring based reconstruction of the Atlantic Multi-decadal Oscillation since 1567 A.D.’

http://www.riversimulator.org/Resources/ClimateDocs/GrayAMO2004.pdf Gray

Gray.NOAA, 2021, Gray, S.T., et al. 2004, Atlantic Multi-decadal Oscillation (AMO) Index Reconstruction, IGBP PAGES/World Data, Center for Paleoclimatology, Data Contribution Series #2004-062, NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. https://www.ncei.noaa.gov/pub/data/paleo/treering/reconstructions/amo-gray2004.txt Gray

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