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Posted on: November 26, 2018



Figure 1: Annual homicides in England and Wales: Full Span 1898-2003figure01figure02


Figure 2: Annual homicides in England and Wales: 1stHalf 1898-1950FIGURE04FIGURE05


Figure 3: Annual homicides in England and Wales: 2ndHalf 1951-2003FIGURE06FIGURE07


Figure 4: HACRUT4 global mean temperature anomaly: Full span 1898-2003hadcru-fullspanhadcru-fullspan-det

Figure 5: HACRUT4 global mean temperature anomaly: 1st Half 1898-1950hadcru1sthalfhadcru-1sthalf-det


Figure 6: HACRUT4 global mean temperature anomaly: 2nd Half 1951-2003hadcru-2ndhalfhadcru-2ndhalf-det


Figure 7: Summary Tables for Figure 1 to Figure 6FIGURE03hadcru-summary






  1. The theory that fossil fuel emissions since the Industrial Revolution have caused global warming is based on the proposition that such emissions increase atmospheric carbon dioxide concentration which in turn increases surface temperature according to a heat trapping effect first proposed by Arrhenius in a failed attempt to explain ice ages. A testable implication of the theory is the Charney Climate Sensitivity equal to the increase in surface temperature for a doubling of atmospheric CO2 and based on the proportionality of surface temperature with the logarithm of atmospheric CO2. This proportionality is described in terms of a linear regression coefficient based on an assumed statistically significant correlation between the two variables.  [RELATED POST ON ECS]
  2. However, the large body of empirical research in climate sensitivity has not produced an orderly accumulation of knowledge but instead created confusion and mistrust of the climate sensitivity parameter by virtue of an unacceptably large range of empirical sensitivity values. The frustration of climate science with this so called “uncertainty issue in climate sensitivity” has motivated proposals to abandon the climate sensitivity approach in favor of the “Climate Response to Cumulative Emissions or TCRE (Transient Climate Response to Cumulative Emissions) (Knutti, 2017) (Matthews, 2009).  [RELATED POST ON TCRE]
  3. This state of affairs in climate sensitivity research is likely the result of insufficient statistical rigor in the research methodologies applied. This work demonstrates spurious proportionalities in time series data that can yield specious climate sensitivities that have no interpretation. A parody of the Charney sensitivity with data for homicides in England and Wales 1898-2003 is used for the demonstration. The homicide parody is compared with a parallel analysis of global mean temperature reconstructions for the same period.
  4. The analysis demonstrates that such spurious results are more likely to be taken seriously when they occur under conditions where they are more likely to be accepted at face value. The results imply that the large number of climate sensitivities reported in the literature are likely to be mostly spurious and without an interpretation in terms of the Charney climate sensitivity. Sufficient statistical discipline is likely to settle the Charney climate sensitivity issue one way or the other, either to determine its hitherto elusive value or to demonstrate that the assumed relationships do not exist in the data.
  5. Homicides in England and Wales 1898-2003 are studied against the atmospheric carbon dioxide data for the same period. The Charney Equilibrium Sensitivity of Homicides is found to be λ=1.7 thousands of additional annual homicides for each doubling of atmospheric CO2. The sensitivity estimate is supported by a strong correlation of ρ=0.95 and detrended correlation of ρ=0.86. The analysis illustrates and demonstrates that spurious proportionalities in time series data derived from inadequate statistical rigor in the interpretation of the data has led to a theory of human caused global warming since the Little Ice Age that is unlikely to survive a review with sufficient statistical rigor.  [RELATED POST ON THE LIA] .
  6. The full text of this work is available for download from [ACADEMIA.EDU] or from [SSRN.COM] . This blog post is a brief presentation of the work and its findings. The discussion consists of a presentation of the seven charts and tables shown above in sequence from Figure 1 to Figure 7.
  7. Figure 1, Figure 2, and Figure 3 present the data for the annual number of homicides in England and Wales for the 106-year period from 1889 to 2003. Each Figure contains two panels (upper and lower) and three frames (left, middle, and right). The upper panel is a presentation of the proportionality between homicides and log(CO2) seen in the source data as received. The lower panel tests that proportionality for responsiveness at an annual time scale with detrended correlation analysis [DESCRIBED IN A RELATED POST] . Each panel consists of three frames. The left frame presents the log(CO2) data, the middle frame presents object data, either homicides or temperature, and the right frame displays their proportionality.
  8. Figure 1: Full Span of the homicide data (1898-2003): The top panel displays the source data with the right frame showing a strong observed correlation in the sample of ρ=0.945 between log(CO2) and the number of homicides per year (in thousands) in the 106-year sample period 1898-2003. This correlation appears to validate the proportionality and in particular, the OLS linear regression coefficient of β=2.45 that represents the homicide sensitivity of to atmospheric CO2. To restate the sensitivity homicides to carbon dioxide in the Charney/Manabe format in terms of a doubling of atmospheric CO2 concentration, we multiply by Ln(2) = 0.694 to find that λ=1.70 thousand additional homicides for each doubling of atmospheric CO2. The 95% confidence interval for the Charney Sensitivity is 95%CI=[1.58<λ<1.81].Thus we find strong empirical support for the proportionality of homicides to atmospheric carbon dioxide concentration that would support a theory that atmospheric CO2 causes homicides.
  9. However, it is known that correlations in time series data are often spurious in this context because these correlations can be driven by shared long term trends with little or no responsiveness information for a finite time scale of interest that is shorter than the full span of the data being studied. Therefore it is necessary to study correlation in time series data net of the trend as a way of extracting the responsiveness information [DESCRIBED IN A RELATED POST] . The lower panel of Figure 1 presents this analysis. The left and middle frames show the detrended series for log(CO2) and thousands of homicides per year. In the detrended series, the OLS linear regression line has been subtracted from the data. The right frame shows the proportionality between the detrended series. What we expect is that some of the correlation seen in the source data is attributable to long term trends but some may remain and if the portion of the correlation that survives into the detrended series is statistically significant, then responsiveness at the time scale of the detrending procedure is implied. In this case, of the source data correlation of r=0.945, a statistically significant ρ=0.859 survives into the detrended series at an annual time scale. The result implies that homicides are responsive to atmospheric CO2 at an annual time scale. Therefore, the source data correlation is not an artifact of shared trends but a result of responsiveness at an annual time scale and can be interpreted in terms of sensitivity of homicides to atmospheric CO2.
  10. Yet another aspect of time series data that must be taken into consideration is the assumption implicit in the full span analysis that the behavior of the data derived from full span analysis is more or less homogeneous across the full span of the data. This condition is imposed by OLS linear regression assumptions. A common method of carrying out the test is the “split-half” test in which the first half and second half of the full span are compared. If they are found to be very different then full span homogeneity cannot be assumed. Figure 2 presents the analysis for the first 53 years of the homicide data 1898-1950. The corresponding analysis for the second half, 1951-2003, is presented in Figure 3. A comparison of these results shows somewhat different sensitivity values particularly in the first half with the Charney Sensitivity λ=[1.70, 0.60, 2.1] thousand additional homicides for each doubling of atmospheric CO2 in the full span, 1st half, and 2nd half of the time series. The detrended correlation supporting the interpretation of these sensitivities at an annual time scale are  ρ=[0.86, 0.28, 0.30]. The strong detrended correlation supporting the regression coefficient seen in the full span is not found in either half of the span and that explains the instability of the regression coefficient in this analysis.
  11. The corresponding analysis of annual HADCRUT4 global mean temperature anomaly data from the Hadley Centre for the same sample period 1898-2003 is presented in Figure 4, Figure 5, and Figure 6. These temperature data are available for q longer period but the sample period studied is that which corresponds with the homicide data so that the same set of CO2 data are used in each case for a common comparison basis. Figure 4 is a graphical display of the analysis for the full span of the data and it shows a regression coefficient of β=3.1 which implies a climate sensitivity of λ=2.15ºC of warming for each doubling of atmospheric CO2. However, the OLS linear regression coefficient is not supported by a sufficient correlation. The full span correlation in the source data is ρ=0.85. However, unlike the homicide data where almost all of the source data correlation survived into the detrended series, almost all of this strong correlation is attributed to the common trend and only ρ=0.27 survives into the detrended series. Thus, although a sensitivity of λ=2.15 can be computed from the data, the existence of sensitivity at an annual time scale is not supported by the data.
  12. The split half analysis of the temperature anomaly data shows a further weakness in the computed climate sensitivity with a dramatic difference between the two halves. The 1st half 1898-1950 shows a very high regression coefficient of β=8.14 that implies an impossibly high climate sensitivity of λ=5.64ºC of warming for each doubling of atmospheric CO2 but no support for the regression is found in the correlation. The significant source data correlation of ρ=0.80 in the source data derives entirely from shared trends and vanishes when detrended leaving a detrended correlation of ρ=0.04 with no statistical significance. The large and anomalous values the regression coefficient and climate sensitivity are likely to be artifacts of violations of OLS assumptions without any interpretation in terms of a relationship between atmospheric CO2 concentration and surface temperature.
  13. Very different results are seen for the 2nd half of the temperature anomaly data 1951-2003 where strong support for climate sensitivity is found. The regression coefficient β=2.77 implies a climate sensitivity of λ=1.92ºC of warming for each doubling of atmospheric CO2 very close to the full span sensitivity of λ=2.15ºC . The sensitivity is supported by a strong and significant source data correlation of ρ=0.81 almost all of which survives into the detrended series with ρ=0.66 . Thus neither the detrended correlation in the full span of the temperature data nor the split half analysis supports the existence of a climate sensitivity parameter in the temperature anomaly data 1898-2003.
  14. Conclusion#1: It is found that the data show stronger support for the parody research question of the sensitivity of homicides to atmospheric CO2 than for the real research question about the sensitivity of surface temperature anomalies to atmospheric CO2. Yet, though there is an overall acceptance of climate sensitivity as being true and proven by data, no one would of course subscribe to the idea of homicide sensitivity. This kind of interpretation of data is a well known property of human cognition called confirmation bias described more fully in a related post [LINK] .
  15. This anomalous result reveals real and possibly serious issues and weaknesses in empirical sensitivity research in climate science in terms of statistics.The weaknesses likely have to do with overlooked OLS linear regression assumptions as well as flawed interpretation of source data correlation in time series data without consideration for the the effect of shared trends on correlation. This consideration is necessary before source data correlation in time series field data are interpreted in terms of causation at a finite time scale. The uncertainty problem in empirical climate sensitivity research likely arises from inadequate attention to whether regression coefficients are supported by correlation at the time scale of interest. Without such support, though regression coefficients may be computed from the data, they have no interpretation in terms of causal relationships. This issue is discussed in detail in related posts [LINK] [LINK]
  16. Conclusion#2: The relationship between correlation in field data and a theory of causation is that correlation at the correct time scale is a necessary but not sufficient condition for causation. This means that that without correlation at the time scale of interest, no causation theory is possible; but it does not mean that correlation at the time scale of interest implies causation. A dramatic demonstration of this principle is provided by the data presented in this work where we find that the homicide parody shows stronger correlation than the climate sensitivity data.
  17. Conclusion#3: The general state of uncertainty and confusion in empirical climate sensitivity research outside of climate models and in the world of observational data may imply that the hypothesized warming effect of atmospheric CO2 concentration, though programmed into climate models, is not supported by observational data and that therefore there is no empirical support for this theory. This conclusion is supported by related posts at this site that may be found at the links that follow: [LINK#1]  ,  [LINK#2]   [LINK#3] [LINK#4]The source paper for this post may be downloaded from [ACADEMIA.EDU] or from [SSRN.COM] 





ECS Bibliography

  1. 1963: Möller, Fritz. “On the influence of changes in the CO2 concentration in air on the radiation balance of the earth’s surface and on the climate.” Journal of Geophysical Research68.13 (1963): 3877-3886. The numerical value of a temperature change under the influence of a CO2 change as calculated by Plass is valid only for a dry atmosphere. Overlapping of the absorption bands of CO2 and H2O in the range around 15 μ essentially diminishes the temperature changes. New calculations give ΔT = + 1.5° when the CO2 content increases from 300 to 600 ppm. Cloudiness diminishes the radiation effects but not the temperature changes because under cloudy skies larger temperature changes are needed in order to compensate for an equal change in the downward long‐wave radiation. The increase in the water vapor content of the atmosphere with rising temperature causes a self‐amplification effect which results in almost arbitrary temperature changes, e.g. for constant relative humidity ΔT = +10° in the above mentioned case. It is shown, however, that the changed radiation conditions are not necessarily compensated for by a temperature change. The effect of an increase in CO2 from 300 to 330 ppm can be compensated for completely by a change in the water vapor content of 3 per cent or by a change in the cloudiness of 1 per cent of its value without the occurrence of temperature changes at all. Thus the theory that climatic variations are effected by variations in the CO2 content becomes very questionable.
  2. 1964: Manabe, Syukuro, and Robert F. Strickler. “Thermal equilibrium of the atmosphere with a convective adjustment.” Journal of the Atmospheric Sciences 21.4 (1964): 361-385. The states of thermal equilibrium (incorporating an adjustment of super-adiabatic stratification) as well as that of pure radiative equilibrium of the atmosphere are computed as the asymptotic steady state approached in an initial value problem. Recent measurements of absorptivities obtained for a wide range of pressure are used, and the scheme of computation is sufficiently general to include the effect of several layers of clouds. The atmosphere in thermal equilibrium has an isothermal lower stratosphere and an inversion in the upper stratosphere which are features observed in middle latitudes. The role of various gaseous absorbers (i.e., water vapor, carbon dioxide, and ozone), as well as the role of the clouds, is investigated by computing thermal equilibrium with and without one or two of these elements. The existence of ozone has very little effect on the equilibrium temperature of the earth’s surface but a very important effect on the temperature throughout the stratosphere; the absorption of solar radiation by ozone in the upper and middle stratosphere, in addition to maintaining the warm temperature in that region, appears also to be necessary for the maintenance of the isothermal layer or slight inversion just above the tropopause. The thermal equilibrium state in the absence of solar insulation is computed by setting the temperature of the earth’s surface at the observed polar value. In this case, the stratospheric temperature decreases monotonically with increasing altitude, whereas the corresponding state of pure radiative equilibrium has an inversion just above the level of the tropopause. A series of thermal equilibriums is computed for the distributions of absorbers typical of different latitudes. According to these results, the latitudinal variation of the distributions of ozone and water vapor may be partly responsible for the latitudinal variation of the thickness of the isothermal part of the stratosphere. Finally, the state of local radiative equilibrium of the stratosphere overlying a troposphere with the observed distribution of temperature is computed for each season and latitude. In the upper stratosphere of the winter hemisphere, a large latitudinal temperature gradient appears at the latitude of the polar-night jet stream, while in the upper statosphere of the summer hemisphere, the equilibrium temperature varies little with latitude. These features are consistent with the observed atmosphere. However, the computations predict an extremely cold polar night temperature in the upper stratosphere and a latitudinal decrease (toward the cold pole) of equilibrium temperature in the middle or lower stratosphere for winter and fall. This disagrees with observation, and suggests that explicit introduction of the dynamics of large scale motion is necessary.
  3. 1967: Manabe, Syukuro, and Richard T. Wetherald. “Thermal equilibrium of the atmosphere with a given distribution of relative humidity.” Journal of the Atmospheric Sciences 24.3 (1967): 241-259. [ECS=2]bandicam 2018-09-21 13-24-28-297
  4. 1969: Budyko, Mikhail I. “The effect of solar radiation variations on the climate of the earth.” tellus 21.5 (1969): 611-619. It follows from the analysis of observation data that the secular variation of the mean temperature of the Earth can be explained by the variation of short-wave radiation, arriving at the surface of the Earth. In connection with this, the influence of long-term changes of radiation, caused by variations of atmospheric transparency on the thermal regime is being studied. Taking into account the influence of changes of planetary albedo of the Earth under the development of glaciations on the thermal regime, it is found that comparatively small variations of atmospheric transparency could be sufficient for the development of quaternary glaciations.
  5. 1969: Sellers, William D. “A global climatic model based on the energy balance of the earth-atmosphere system.” Journal of Applied Meteorology 8.3 (1969): 392-400. A relatively simple numerical model of the energy balance of the earth-atmosphere is set up and applied. The dependent variable is the average annual sea level temperature in 10° latitude belts. This is expressed basically as a function of the solar constant, the planetary albedo, the transparency of the atmosphere to infrared radiation, and the turbulent exchange coefficients for the atmosphere and the oceans. The major conclusions of the analysis are that removing the arctic ice cap would increase annual average polar temperatures by no more than 7C, that a decrease of the solar constant by 2–5% might be sufficient to initiate another ice age, and that man’s increasing industrial activities may eventually lead to a global climate much warmer than today.
  6. 1971: Rasool, S. Ichtiaque, and Stephen H. Schneider. “Atmospheric carbon dioxide and aerosols: Effects of large increases on global climate.” Science 173.3992 (1971): 138-141. Effects on the global temperature of large increases in carbon dioxide and aerosol densities in the atmosphere of Earth have been computed. It is found that, although the addition of carbon dioxide in the atmosphere does increase the surface temperature, the rate of temperature increase diminishes with increasing carbon dioxide in the atmosphere. For aerosols, however, the net effect of increase in density is to reduce the surface temperature of Earth. Because of the exponential dependence of the backscattering, the rate of temperature decrease is augmented with increasing aerosol content. An increase by only a factor of 4 in global aerosol background concentration may be sufficient to reduce the surface temperature by as much as 3.5 ° K. If sustained over a period of several years, such a temperature decrease over the whole globe is believed to be sufficient to trigger an ice age.
  7. 1975: Manabe, Syukuro, and Richard T. Wetherald. “The effects of doubling the CO2 concentration on the climate of a general circulation model.” Journal of the Atmospheric Sciences 32.1 (1975): 3-15. An attempt is made to estimate the temperature changes resulting from doubling the present CO2 concentration by the use of a simplified three-dimensional general circulation model. This model contains the following simplifications: a limited computational domain, an idealized topography, no beat transport by ocean currents, and fixed cloudiness. Despite these limitations, the results from this computation yield some indication of how the increase of CO2 concentration may affect the distribution of temperature in the atmosphere. It is shown that the CO2 increase raises the temperature of the model troposphere, whereas it lowers that of the model stratosphere. The tropospheric warming is somewhat larger than that expected from a radiative-convective equilibrium model. In particular, the increase of surface temperature in higher latitudes is magnified due to the recession of the snow boundary and the thermal stability of the lower troposphere which limits convective beating to the lowest layer. It is also shown that the doubling of carbon dioxide significantly increases the intensity of the hydrologic cycle of the model. bandicam 2018-09-21 15-17-14-922
  8. 1976: Cess, Robert D. “Climate change: An appraisal of atmospheric feedback mechanisms employing zonal climatology.” Journal of the Atmospheric Sciences 33.10 (1976): 1831-1843. The sensitivity of the earth’s surface temperature to factors which can induce long-term climate change, such as a variation in solar constant, is estimated by employing two readily observable climate changes. One is the latitudinal change in annual mean climate, for which an interpretation of climatological data suggests that cloud amount is not a significant climate feedback mechanism, irrespective of how cloud amount might depend upon surface temperature, since there are compensating changes in both the solar and infrared optical properties of the atmosphere. It is further indicated that all other atmospheric feedback mechanisms, resulting, for example, from temperature-induced changes in water vapor amount, cloud altitude and lapse rate, collectively double the sensitivity of global surface temperature to a change in solar constant. The same conclusion is reached by considering a second type of climate change, that associated with seasonal variations for a given latitude zone. The seasonal interpretation further suggests that cloud amount feedback is unimportant zonally as well as globally. Application of the seasonal data required a correction for what appears to be an important seasonal feedback mechanism. This is attributed to a variability in cloud albedo due to seasonal changes in solar zenith angle. No attempt was made to individually interpret the collective feedback mechanisms which contribute to the doubling in surface temperature sensitivity. It is suggested, however, that the conventional assumption of fixed relative humidity for describing feedback due to water vapor amount might not be as applicable as is generally believed. Climate models which additionally include ice-albedo feedback are discussed within the framework of the present results.
  9. 1978: Ramanathan, V., and J. A. Coakley. “Climate modeling through radiative‐convective models.” Reviews of geophysics16.4 (1978): 465-489. We present a review of the radiative‐convective models that have been used in studies pertaining to the earth’s climate. After familiarizing the reader with the theoretical background, modeling methodology, and techniques for solving the radiative transfer equation the review focuses on the published model studies concerning global climate and global climate change. Radiative‐convective models compute the globally and seasonally averaged surface and atmospheric temperatures. The computed temperatures are in good agreement with the observed temperatures. The models include the important climatic feedback mechanism between surface temperature and H2O amount in the atmosphere. The principal weakness of the current models is their inability to simulate the feedback mechanism between surface temperature and cloud cover. It is shown that the value of the critical lapse rate adopted in radiative‐convective models for convective adjustment is significantly larger than the observed globally averaged tropospheric lapse rate. The review also summarizes radiative‐convective model results for the sensitivity of surface temperature to perturbations in (1) the concentrations of the major and minor optically active trace constituents, (2) aerosols, and (3) cloud amount. A simple analytical model is presented to demonstrate how the surface temperature in a radiative‐convective model responds to perturbations.
  10. 1985: Wigley, Thomas ML, and Michael E. Schlesinger. “Analytical solution for the effect of increasing CO2 on global mean temperature.” Nature 315.6021 (1985): 649. Increasing atmospheric carbon dioxide concentration is expected to cause substantial changes in climate. Recent model studies suggest that the equilibrium warming for a CO2 doubling (Δ T2×) is about 3–4°C. Observational data show that the globe has warmed by about 0.5°C over the past 100 years. Are these two results compatible? To answer this question due account must be taken of oceanic thermal inertia effects, which can significantly slow the response of the climate system to external forcing. The main controlling parameters are the effective diffusivity of the ocean below the upper mixed layer (κ) and the climate sensitivity (defined by Δ T2×). Previous analyses of this problem have considered only limited ranges of these parameters. Here we present a more general analysis of two cases, forcing by a step function change in CO2 concentration and by a steady CO2 increase. The former case may be characterized by a response time which we show is strongly dependent on both κ and Δ T2×. In the latter case the damped response means that, at any given time, the climate system may be quite far removed from its equilibrium with the prevailing CO2 level. In earlier work this equilibrium has been expressed as a lag time, but we show this to be misleading because of the sensitivity of the lag to the history of past CO2 variations. Since both the lag and the degree of disequilibrium are strongly dependent on κ and Δ T2×, and because of uncertainties in the pre-industrial CO2 level, the observed global warming over the past 100 years can be shown to be compatible with a wide range of CO2-doubling temperature changes.
  11. 1991: Lawlor, D. W., and R. A. C. Mitchell. “The effects of increasing CO2 on crop photosynthesis and productivity: a review of field studies.” Plant, Cell & Environment 14.8 (1991): 807-818. Only a small proportion of elevated CO2 studies on crops have taken place in the field. They generally confirm results obtained in controlled environments: CO2increases photosynthesis, dry matter production and yield, substantially in C3 species, but less in C4, it decreases stomatal conductance and transpiration in C3 and C4 species and greatly improves water‐use efficiency in all plants. The increased productivity of crops with CO2 enrichment is also related to the greater leaf area produced. Stimulation of yield is due more to an increase in the number of yield‐forming structures than in their size. There is little evidence of a consistent effect of CO2 on partitioning of dry matter between organs or on their chemical composition, except for tubers. Work has concentrated on a few crops (largely soybean) and more is needed on crops for which there are few data (e.g. rice). Field studies on the effects of elevated CO2 in combination with temperature, water and nutrition are essential; they should be related to the development and improvement of mechanistic crop models, and designed to test their predictions.
  12. 2009: Danabasoglu, Gokhan, and Peter R. Gent. “Equilibrium climate sensitivity: Is it accurate to use a slab ocean model?.” Journal of Climate 22.9 (2009): 2494-2499. The equilibrium climate sensitivity of a climate model is usually defined as the globally averaged equilibrium surface temperature response to a doubling of carbon dioxide. This is virtually always estimated in a version with a slab model for the upper ocean. The question is whether this estimate is accurate for the full climate model version, which includes a full-depth ocean component. This question has been answered for the low-resolution version of the Community Climate System Model, version 3 (CCSM3). The answer is that the equilibrium climate sensitivity using the full-depth ocean model is 0.14°C higher than that using the slab ocean model, which is a small increase. In addition, these sensitivity estimates have a standard deviation of nearly 0.1°C because of interannual variability. These results indicate that the standard practice of using a slab ocean model does give a good estimate of the equilibrium climate sensitivity of the full CCSM3. Another question addressed is whether the effective climate sensitivity is an accurate estimate of the equilibrium climate sensitivity. Again the answer is yes, provided that at least 150 yr of data from the doubled carbon dioxide run are used.
  13. 2010: Connell, Sean D., and Bayden D. Russell. “The direct effects of increasing CO2 and temperature on non-calcifying organisms: increasing the potential for phase shifts in kelp forests.” Proceedings of the Royal Society of London B: Biological Sciences (2010): rspb20092069. Predictions about the ecological consequences of oceanic uptake of CO2 have been preoccupied with the effects of ocean acidification on calcifying organisms, particularly those critical to the formation of habitats (e.g. coral reefs) or their maintenance (e.g. grazing echinoderms). This focus overlooks the direct effects of CO2 on non-calcareous taxa, particularly those that play critical roles in ecosystem shifts. We used two experiments to investigate whether increased CO2 could exacerbate kelp loss by facilitating non-calcareous algae that, we hypothesized, (i) inhibit the recovery of kelp forests on an urbanized coast, and (ii) form more extensive covers and greater biomass under moderate future CO2 and associated temperature increases. Our experimental removal of turfs from a phase-shifted system (i.e. kelp- to turf-dominated) revealed that the number of kelp recruits increased, thereby indicating that turfs can inhibit kelp recruitment. Future CO2 and temperature interacted synergistically to have a positive effect on the abundance of algal turfs, whereby they had twice the biomass and occupied over four times more available space than under current conditions. We suggest that the current preoccupation with the negative effects of ocean acidification on marine calcifiers overlooks potentially profound effects of increasing CO2and temperature on non-calcifying organisms.
  14. 2011: Schmittner, Andreas, et al. “Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum.” Science 334.6061 (2011): 1385-1388. Assessing the impact of future anthropogenic carbon emissions is currently impeded by uncertainties in our knowledge of equilibrium climate sensitivity to atmospheric carbon dioxide doubling. Previous studies suggest 3 kelvin (K) as the best estimate, 2 to 4.5 K as the 66% probability range, and nonzero probabilities for much higher values, the latter implying a small chance of high-impact climate changes that would be difficult to avoid. Here, combining extensive sea and land surface temperature reconstructions from the Last Glacial Maximum with climate model simulations, we estimate a lower median (2.3 K) and reduced uncertainty (1.7 to 2.6 K as the 66% probability range, which can be widened using alternate assumptions or data subsets). Assuming that paleoclimatic constraints apply to the future, as predicted by our model, these results imply a lower probability of imminent extreme climatic change than previously thought.
  15. 2012: Fasullo, John T., and Kevin E. Trenberth. “A less cloudy future: The role of subtropical subsidence in climate sensitivity.” science 338.6108 (2012): 792-794. An observable constraint on climate sensitivity, based on variations in mid-tropospheric relative humidity (RH) and their impact on clouds, is proposed. We show that the tropics and subtropics are linked by teleconnections that induce seasonal RH variations that relate strongly to albedo (via clouds), and that this covariability is mimicked in a warming climate. A present-day analog for future trends is thus identified whereby the intensity of subtropical dry zones in models associated with the boreal monsoon is strongly linked to projected cloud trends, reflected solar radiation, and model sensitivity. Many models, particularly those with low climate sensitivity, fail to adequately resolve these teleconnections and hence are identifiably biased. Improving model fidelity in matching observed variations provides a viable path forward for better predicting future climate.
  16. 2012: Andrews, Timothy, et al. “Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models.” Geophysical Research Letters 39.9 (2012). We quantify forcing and feedbacks across available CMIP5 coupled atmosphere‐ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing‐feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top‐of‐atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea‐surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time‐scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.
  17. 2012: Bitz, Cecilia M., et al. “Climate sensitivity of the community climate system model, version 4.” Journal of Climate 25.9 (2012): 3053-3070.Equilibrium climate sensitivity of the Community Climate System Model, version 4 (CCSM4) is 3.20°C for 1° horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1° resolution is 1.72°C, which is about 0.2°C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. This study uses the radiative kernel technique to show that, from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude and the shortwave cloud feedback increases. These two warming effects are partially canceled by cooling because of slight decreases in the global mean water vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed layer, slab-ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab-ocean model version for both CCSM3 and CCSM4. The authors argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.
  18. 2012: Rogelj, Joeri, Malte Meinshausen, and Reto Knutti. “Global warming under old and new scenarios using IPCC climate sensitivity range estimates.” Nature climate change 2.4 (2012): 248. Climate projections for the fourth assessment report1 (AR4) of the Intergovernmental Panel on Climate Change (IPCC) were based on scenarios from the Special Report on Emissions Scenarios2 (SRES) and simulations of the third phase of the Coupled Model Intercomparison Project3 (CMIP3). Since then, a new set of four scenarios (the representative concentration pathways or RCPs) was designed4. Climate projections in the IPCC fifth assessment report (AR5) will be based on the fifth phase of the Coupled Model Intercomparison Project5 (CMIP5), which incorporates the latest versions of climate models and focuses on RCPs. This implies that by AR5 both models and scenarios will have changed, making a comparison with earlier literature challenging. To facilitate this comparison, we provide probabilistic climate projections of both SRES scenarios and RCPs in a single consistent framework. These estimates are based on a model set-up that probabilistically takes into account the overall consensus understanding of climate sensitivity uncertainty, synthesizes the understanding of climate system and carbon-cycle behaviour, and is at the same time constrained by the observed historical warming.
  19. 2014: Sherwood, Steven C., Sandrine Bony, and Jean-Louis Dufresne. “Spread in model climate sensitivity traced to atmospheric convective mixing.” Nature 505.7481 (2014): 37. Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.
  20. 2015: Mauritsen, Thorsten, and Bjorn Stevens. “Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models.” Nature Geoscience 8.5 (2015): 346. Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6 K in current climate models, and they suggest a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity near the lower end of this range and indicate that models underestimate some of the changes in the hydrological cycle. These discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation to escape to space. This so-called iris effect could constitute a negative feedback that is not included in climate models. We find that inclusion of such an effect in a climate model moves the simulated responses of both temperature and the hydrological cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of models — such as aerosol cooling, volcanic eruptions or insufficient ocean heat uptake — may explain a slow observed transient warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating convective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible physical mechanism for an iris effect.
  21. 2015: Schimel, David, Britton B. Stephens, and Joshua B. Fisher. “Effect of increasing CO2 on the terrestrial carbon cycle.” Proceedings of the National Academy of Sciences 112.2 (2015): 436-441. Feedbacks from terrestrial ecosystems to atmospheric CO2 concentrations contribute the second-largest uncertainty to projections of future climate. These feedbacks, acting over huge regions and long periods of time, are extraordinarily difficult to observe and quantify directly. We evaluated in situ, atmospheric, and simulation estimates of the effect of CO2 on carbon storage, subject to mass balance constraints. Multiple lines of evidence suggest significant tropical uptake for CO2, approximately balancing net deforestation and confirming a substantial negative global feedback to atmospheric CO2 and climate. This reconciles two approaches that have previously produced contradictory results. We provide a consistent explanation of the impacts of CO2 on terrestrial carbon across the 12 orders of magnitude between plant stomata and the global carbon cycle.
  22. 2016: Tan, Ivy, Trude Storelvmo, and Mark D. Zelinka. “Observational constraints on mixed-phase clouds imply higher climate sensitivity.” Science 352.6282 (2016): 224-227. How much global average temperature eventually will rise depends on the Equilibrium Climate Sensitivity (ECS), which relates atmospheric CO2 concentration to atmospheric temperature. For decades, ECS has been estimated to be between 2.0° and 4.6°C, with much of that uncertainty owing to the difficulty of establishing the effects of clouds on Earth’s energy budget. Tan et al. used satellite observations to constrain the radiative impact of mixed phase clouds. They conclude that ECS could be between 5.0° and 5.3°C—higher than suggested by most global climate models.
  23. 2018: Watanabe, Masahiro, et al. “Low clouds link equilibrium climate sensitivity to hydrological sensitivity.” Nature Climate Change (2018): 1. Equilibrium climate sensitivity (ECS) and hydrological sensitivity describe the global mean surface temperature and precipitation responses to a doubling of atmospheric CO2. Despite their connection via the Earth’s energy budget, the physical linkage between these two metrics remains controversial. Here, using a global climate model with a perturbed mean hydrological cycle, we show that ECS and hydrological sensitivity per unit warming are anti-correlated owing to the low-cloud response to surface warming. When the amount of low clouds decreases, ECS is enhanced through reductions in the reflection of shortwave radiation. In contrast, hydrological sensitivity is suppressed through weakening of atmospheric longwave cooling, necessitating weakened condensational heating by precipitation. These compensating cloud effects are also robustly found in a multi-model ensemble, and further constrained using satellite observations. Our estimates, combined with an existing constraint to clear-sky shortwave absorption, suggest that hydrological sensitivity could be lower by 30% than raw estimates from global climate mode





[…] SPURIOUS CORRELATIONS IN TIME SERIES DATA: Unrelated time series data can show spurious correlations by virtue of a shared drift in the long term trend. The spuriousness of such correlations is demonstrated with examples. The SP500 stock market index, GDP at current prices for the USA, and the number of homicides in England and Wales in the sample period 1968 to 2002 are used for this demonstration. Detrended analysis shows the expected result that at an annual time scale the GDP and SP500 series are related and that neither of these time series is related to the homicide series. Correlations between the source data and those between cumulative values show spurious correlations of the two financial time series with the homicide series. These results have implications for empirical evidence that attributes changes in temperature and carbon dioxide levels in the surface-atmosphere system to fossil fuel emissions. FULL TEXT. Yet another example of spurious correlations in time series data is the apparent homicide sensitivity of atmospheric carbon dioxide concentration described in a related post [LINK] . […]

[…] We conclude that no evidence is found in the observational data to indicate that either tropospheric warming or lower stratospheric cooling is responsive to changes in LN(CO2) or that stratospheric cooling is responsive to tropospheric warming, at an annual time scale. These data do not support the theory of causation that links stratospheric cooling to tropospheric warming or the causal effect of atmospheric CO2 concentration on either of these temperatures. Two related posts on the effect of atmospheric CO2 on temperature are relevant to these findings [LINK] [LINK] . […]

[…] The heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century.  This statement is false. The works of Arrhenius, Hogbom, Tyndal, Langley, and others were a failed attempt to explain glaciation cycles over long periods of time but relationship was never demonstrated either by them or by modern climate science or by NASA. It has been formalized into the so called Climate Sensitivity by Manabe, Charney, and others and is found in climate models where the CO2 heat trapping effect is included, but attempts to find it in observational data has been thwarted by an unacceptable level of uncertainty as shown in this related post [LINK] . So great is this uncertainty problem that it motivated top climate scientists to declare that it was time to leave the climate sensitivity issue behind and move on to the TCRE which is a more stable metric. See 2017: Knutti, Reto, Maria AA Rugenstein, and Gabriele C. Hegerl. “Beyond equilibrium climate sensitivity.” Nature Geoscience10.10. A related post shows that a statistically significant climate sensitivity is found in the RCP8.5 theoretical series that was derived from this assumption but is not found in the observational data [LINK] [LINK] […]

[…] The heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century.  This statement is false. The works of Arrhenius, Hogbom, Tyndal, Langley, and others were a failed attempt to explain glaciation cycles over long periods of time but relationship was never demonstrated either by them or by modern climate science or by NASA. It has been formalized into the so called Climate Sensitivity by Manabe, Charney, and others and is found in climate models where the CO2 heat trapping effect is included, but attempts to find it in observational data has been thwarted by an unacceptable level of uncertainty as shown in this related post [LINK] . So great is this uncertainty problem that it motivated top climate scientists to declare that it was time to leave the climate sensitivity issue behind and move on to the TCRE which is a more stable metric. See 2017: Knutti, Reto, Maria AA Rugenstein, and Gabriele C. Hegerl. “Beyond equilibrium climate sensitivity.” Nature Geoscience10.10. A related post shows that a statistically significant climate sensitivity is found in the RCP8.5 theoretical series that was derived from this assumption but is not found in the observational data [LINK][LINK] […]

[…] INTRODUCTION: The science of climate change by human activity in the industrial economy in terms of the combustion of fossil fuels rests on two critical relationships. The first is that the observed rise in atmospheric CO2 concentration is driven by CO2 emissions from fossil fuels; and the second is that the observed rise in surface temperature is driven by the higher levels of atmospheric CO2 thus created. The relationship between atmospheric CO2 concentration and surfae temperature is thought to be governed by the so called “greenhouse effect” which implies that surface temperature is responsive to the logarithm of atmospheric CO2 concentration in a positive relationship such that higher atmospheric CO2 concentration generates higher surface temperature. This relationship is expressed as “climate sensitivity” and computed as the the increase in surface temperature in Celsius units for each doubling of atmospheric carbon dioxide. The first of these two critical relationships in climate change is discussed in a related post on this site [LINK] . This post is a presentation of the second relationship in the form of an empirical test with global mean temperature reconstructions. This issue is also presented in some related posts on this site  [LINK]  [LINK]  [LINK]  [LINK] . […]

[…] In terms of AGW theory, the relationship between atmospheric CO2 concentration and the rate of warming is established with the so called climate sensitivity parameter (ECS) derived from the required linear relationship between temperature and log(atmospheric CO2) but the value of this relationship, even in climate models, suffers from uncertainty with values ranging from ECS<2 to ECS>4. When observational data are used in the estimation the uncertainty becomes much larger as described in related posts [LINK] [LINK] [LINK] [LINK] .  In a parody it is shown that the methodology used to relate warming to CO2 also relates homicides to CO2 but with stronger statistical confidence [LINK] . […]

[…] The methodology used by climate science to show that warming is driven by CO2 also shows that homicides are driven by CO2 [LINK] […]

[…] CLAIM#5: Christy & McNider (2017) and Lewis & Curry (2018) have shown that the maximum possible value of Equilibrium Climate Sensitivity is ECS=1 but climate scientists have presented AGW theory and its catastrophic consequences based on sensitivity values of 3<ECS<5, much higher than ECS=1. Therefore AGW is false and simply a fear mongering device because no dangerous runaway warming is possible at ECS≤1.  RESPONSE: The low values of ECS reported here are not anything new as a review of the ECS literature that goes back to Manabe and Wetherald 1964 shows. The extant literature shows ECS values over a large range that includes ECS≤1.  In related posts on this site are cited a large number of works that report ECS values of ECS<1 to ECS>10   [LINK] [LINK] [LINK] [LINK] . A specific issue in the literature is found in Andronova 2000 where she reports ECS = [2.0-5.0] with the note that more than half of that figure can be explained by solar variability. That leaves her with residual CO2 sensitivity ECS=[0.94-2.35]. This finding weakens the role of human cause in AGW but in the context of a body of research that has failed to identify the value of ECS. The real ECS issue may therefore be not what its value is but whether such a parameter exists. Please see: [LINK] [LINK] [LINK] [LINK] […]

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