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![Temperature (T) and atmospheric carbon dioxide (CO2) concentration proxies during the Phanerozoic Eon. Time series of the global temperature proxy (δ 18 O*(−1), red curve, n = 6680) are from Prokoph et al. [28] while concurrent atmospheric CO2 concentration proxies (green curve, n = 831) are from Royer [40]. The red curve plots moving averages of the non-detrended T proxy averaged in 50 My windows incremented in ten-My steps (the 10–50 My moving average in Figure 3b). The green curve shows mean CO2 concentration values in one-My bins averaged over high-resolution portions of the CO2 record (the most recent Phanerozoic) and linearly interpolated over low-resolution portions (the older Phanerozoic). Glaciations based on independent sedimentary evidence are designated by vertical blue cross-hatched areas, while putative cool periods are designated by vertical solid blue bars. Major cooling and warming cycles are shown by the colored bars across the top while geological periods and evolutionary milestones are shown across the bottom. Abbreviations: CO2, atmospheric concentration of carbon dioxide based on various proxies (Methods); ppmv, parts per million by volume; Silu, Silurian; Neo, Neogene; Quatern, Quaternary. The three major glacial periods and ten cooling periods identified by blue cross-hatches and solid lines, respectively, are (after [21]): Glacial periods. 1. late Devonian/early Carboniferous; 2. Permo-Carboniferous; 3. late Cenozoic. Cooling periods. 1. late Pliensbachian; 2. Bathonian; 3. late Callovian to mid-Oxfordian; 4. Tithonian to early Berriasian; 5. Aptian; 6. mid-Cenomanian; 7. mid-Turonian; 8. Campanian-Maastrichtian boundary; 9. mid-Maastrichtian; 10. late-Maastrichtian.](publication/320123470/figure/fig4/AS:544116959789056@1506739000327/Temperature-T-and-atmospheric-carbon-dioxide-CO2-concentration-proxies-during-the.png)
Temperature (T) and atmospheric carbon dioxide (CO2) concentration proxies during the Phanerozoic Eon. Time series of the global temperature proxy (δ 18 O*(−1), red curve, n = 6680) are from Prokoph et al. [28] while concurrent atmospheric CO2 concentration proxies (green curve, n = 831) are from Royer [40]. The red curve plots moving averages of the non-detrended T proxy averaged in 50 My windows incremented in ten-My steps (the 10–50 My moving average in Figure 3b). The green curve shows mean CO2 concentration values in one-My bins averaged over high-resolution portions of the CO2 record (the most recent Phanerozoic) and linearly interpolated over low-resolution portions (the older Phanerozoic). Glaciations based on independent sedimentary evidence are designated by vertical blue cross-hatched areas, while putative cool periods are designated by vertical solid blue bars. Major cooling and warming cycles are shown by the colored bars across the top while geological periods and evolutionary milestones are shown across the bottom. Abbreviations: CO2, atmospheric concentration of carbon dioxide based on various proxies (Methods); ppmv, parts per million by volume; Silu, Silurian; Neo, Neogene; Quatern, Quaternary. The three major glacial periods and ten cooling periods identified by blue cross-hatches and solid lines, respectively, are (after [21]): Glacial periods. 1. late Devonian/early Carboniferous; 2. Permo-Carboniferous; 3. late Cenozoic. Cooling periods. 1. late Pliensbachian; 2. Bathonian; 3. late Callovian to mid-Oxfordian; 4. Tithonian to early Berriasian; 5. Aptian; 6. mid-Cenomanian; 7. mid-Turonian; 8. Campanian-Maastrichtian boundary; 9. mid-Maastrichtian; 10. late-Maastrichtian.
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Assessing human impacts on climate and biodiversity requires an understanding of the relationship between the concentration of carbon dioxide (CO2) in the Earth’s atmosphere and global temperature (T). Here I explore this relationship empirically using comprehensive, recently-compiled databases of stable-isotope proxies from the Phanerozoic Eon (~5...
Contexts in source publication
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... and atmospheric CO2 concentration proxies plotted in the same time series panel ( Figure 5) show an apparent dissociation and even an antiphasic relationship. For example, a CO2 concentration peak near 415 My occurs near a temperature trough at 445 My. ...
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... example, a CO2 concentration peak near 415 My occurs near a temperature trough at 445 My. Similarly, CO2 concentration peaks around 285 Mybp coincide with a temperature trough at about 280 My and also with the Permo-Carboniferous glacial period (labeled 2 in Figure 5). In more recent time periods, where data sampling resolution is greater, the same trend is visually evident. ...
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... atmospheric CO2 concentration peak near 200 My occurs during a cooling climate, as does another, smaller CO2 concentration peak at approximately 37 My. The shorter cooling periods of the Phanerozoic, labeled 1-10 in Figure 5, do not appear qualitatively, at least, to bear any definitive relationship with fluctuations in the atmospheric concentration of CO2. ...
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... to the conventional expectation, therefore, as the concentration of atmospheric CO2 increased during the Phanerozoic climate, T decreased. This finding is consistent with the apparent weak antiphasic relation between atmospheric CO2 concentration proxies and T suggested by visual examination of empirical data ( Figure 5). The percent of variance in T that can be explained by variance in atmospheric CO2 concentration, or conversely, R 2 × 100, is 3.6% ( Figure 6). ...
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... correlation coefficients between the concentration of CO2 in the atmosphere and T were computed also across 15 shorter time segments of the Phanerozoic. These time periods were selected to include or bracket the three major glacial periods of the Phanerozoic, ten global cooling events identified by stratigraphic indicators, and major transitions between warming and cooling of the Earth designated by the bar across the top of Figure 5. The analysis was done separately for the most recent time periods of the Phanerozoic, where the sampling resolution was highest (Table 1), and for the older time periods of the Phanerozoic, where the sampling resolution was lower (Table 2). ...
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... scale for the temperature proxy is modified here by a constant offset for graphical presentation on the same scales as the other proxies. The correct temperature-proxy scale is shown in Figure 5. In this and following graphs, climate variables are color-coded: red represents the temperature proxy, δ 18 O*(−1); green represents atmospheric CO2 concentration proxies converted to ppmv; and orange represents the marginal forcing calculated for each atmospheric CO2 concentration datapoint using the best-fit power function equation corresponding to the empirical curve for tropical latitudes under clear-sky conditions (Figure 8b). ...
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... between δ 18 O*(−1) and atmospheric CO2 concentration (Figure 14) shows periodicity on long (Figure 14a) and short (Figure 14b) time scales at respective cycle periods of 70.0 and 16.0 My. The cross-correlation between δ 18 O*(−1) and ΔRFCO2 (Figure 15) show similar periodicity with long and short cycles at about 55.0 and 17.0 My (Figure 15a,b respectively). The cross-correlation between atmospheric CO2 concentration and ΔRFCO2, in contrast, shows strongly negative correlation coefficients at low-order lags that maximize at zero order lag, signifying the precise antiphasic relationship between these variables that is qualitatively evident in Figure 11. ...
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... between δ 18 O*(−1) and atmospheric CO2 concentration (Figure 14) shows periodicity on long (Figure 14a) and short (Figure 14b) time scales at respective cycle periods of 70.0 and 16.0 My. The cross-correlation between δ 18 O*(−1) and ΔRFCO2 (Figure 15) show similar periodicity with long and short cycles at about 55.0 and 17.0 My (Figure 15a,b respectively). The cross-correlation between atmospheric CO2 concentration and ΔRFCO2, in contrast, shows strongly negative correlation coefficients at low-order lags that maximize at zero order lag, signifying the precise antiphasic relationship between these variables that is qualitatively evident in Figure 11. ...
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... second study [67] concluded that the current high rate of CO2 emissions risks atmospheric concentrations not seen for 50 My by the middle of the present century. In contrast to that conclusion, the current rate of CO2 emissions is raising atmospheric concentration of CO2 by 1-2 ppmv per annum [1], implying an atmospheric concentration of CO2 of 440 to 463 ppmv by the middle of the present century-concentrations that appear repeatedly over the last ten My and as recently as three Mybp in the CO2 databases used here ( Figure 5). The same study [67] concluded that if additional emissions of CO2 at current rates continued until the 23rd century, they would cause large and potentially hazardous increases in RFCO2. ...
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... third study relevant to the present investigation [68] addresses uncertainties in paleo-measurements of atmospheric CO2 concentration using "mathematical equations", and concludes that large variations in atmospheric CO2 concentration (1000-2000 ppmv) did not occur during the Phanerozoic Eon since the Devonian Period from 416 to 358 Mybp. In contrast to that conclusion [68], the empirical database used in the present study shows that variations in atmospheric CO2 concentration of 1000-2000 ppmv were common throughout the Phanerozoic Eon ( Figure 5). Atmospheric CO2 concentration oscillated on a regular cycle of several hundreds of ppmv in amplitude and periods of 10-20 My and 60-70 My. ...
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... if anthropogenic CO2 emissions continue at today's levels or increase in the coming decades, the consequent increasing concentration of CO2 in the atmosphere from anthropogenic sources will have exponentially smaller forcing impact on global temperature. As implied by the decline in marginal forcing, when atmospheric CO2 concentration reaches 1000 ppmv, near the baseline value for most of the Phanerozoic Eon (Figure 5), marginal forcing will decline to 14.3% of its maximum (computed using MODTRAN; Figure 8b). Such diminishing returns ensure that additional increments in anthropogenic emissions from today's level will have exponentially smaller marginal impact on global temperature. ...
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The UK Net Zero by 2050 Policy was undemocratically adopted by the UK government in 2019. Yet the science of so-called ‘greenhouse gases’ is well known and there is no reason to reduce emissions of carbon dioxide (CO2), methane (CH4), or nitrous oxide (N2O) because absorption of radiation is logarithmic. Adding to or removing these naturally occurring gases from the atmosphere will make little difference to the temperature or the climate. Water vapor (H2O) is claimed to be a much stronger ‘greenhouse gas’ than CO2, CH4 or N2O but cannot be regulated because it occurs naturally in vast quantities. This work explores the established science and recent developments in scientific knowledge around Net Zero with a view to making a rational recommendation for policy makers. There is little scientific evidence to support the case for Net Zero and that greenhouse gases are unlikely to contribute to a ‘climate emergency’ at current or any likely future higher concentrations. There is a case against the adoption of Net Zero given the enormous costs associated with implementing the policy, and the fact it is unlikely to achieve reductions in average near surface global air temperature, regardless of whether Net Zero is fully implemented and adopted worldwide. Therefore, Net Zero does not pass the cost-benefit test. The recommended policy is to abandon Net Zero and do nothing about so-called ‘greenhouse gases’.
... Two example graphs used in various publications are shown in Figure 1. The first chart is over a geologic timescale of 100s of millions of years, which shows that the current CO 2 level in the Earth's atmosphere is actually at a low point [9]. Interestingly, the surface temperature and the atmospheric CO 2 concentrations did not correlate over much of geologic time [10] (even though apparent correlations may be inferred for much shorter timescales [11]). ...
... A comparison of the two charts shows that these types of rises and falls can be seen in the older data and that such changes are not unique to recent decades. [9]) and recent measurement range of 310-430 ppm (right image, reproduced from NOAA h ps://gml.noaa.gov/ccgg/trends/ (accessed on 30 September 2024). ...
... This implies that to predict future change, a be er understanding is needed of where the additional carbon added has gone and whether this has a net positive or negative effect. [9]) and recent measurement range of 310-430 ppm (right image, reproduced from NOAA https://gml.noaa.gov/ccgg/trends/ (accessed on 30 September 2024). ...
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... CO 2 is a greenhouse gas which has influence on climate and global temperature in complex cause and effect relationships [3][4][5][6][7]. For this motivation, CO 2 is injected into subsurface porous formations -Geological Carbon Sequestration (including marine geological sequestration [8,9]), effectively reducing the amount that would otherwise be emitted into the atmosphere [10-12]. ...
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Open Porous Media (OPM) Flow is an open-source reservoir simulator used for solving subsurface porous media flow problems. Focus is placed here on carbon sequestration and the modeling of fluid flow within underground porous reservoirs. In this study, a sensitivity analysis of some input parameters for carbon sequestration is performed using six different uncertain parameters. An ensemble of model realizations is simulated using OPM Flow, and the model output is then calculated based on the values of the six input parameters mentioned above. CO2 injection is simulated for a period of 15 years, while the post-injection migration of CO2 in the saline storage aquifer is simulated for a subsequent period of 200 years, leading to a final analysis after 215 years. The input parameter values are generated using the quasi-Monte Carlo (QMC) method in the region of interest, following specified patterns suitable for analysis. The optimal convergence rate for quasi-Monte Carlo is observed. The aim of this study is to identify important input parameters contributing significantly to the model output, which is accomplished using sensitivity analysis and verified through symbolic regression modeling based on machine learning. Global sensitivity analysis using the Sobol sequence identifies input parameter 3, 'Permeability of shale between sand layers,' as having the most influence on the model output 'Secondary Trapped CO2.' All regression models, including the simplest and least accurate ones, incorporate parameter 3, confirming its significance. These approximations are valid within the designated area of interest for the input parameters and are easily interpretable for human experts. Sensitivity analysis of the developed time-dependent carbon sequestration model shows that the significance of each physical parameter changes over time: Sand porosity is more significant than shale permeability for roughly the first 120 years. Consequently, the presented results show that simulation timescales of at least 200 years are necessary for carbon sequestration evaluation.
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... A compilation of about 1500 discrete estimates of atmospheric CO₂ from 112 published studies covering the last 420 million years, was presented by Foster et al. [42]. Here, as shown in Figure 2, I retrieved three different time series published in the last 16 years, namely by Royer [43] (also contained in Davis [44]), Foster et al. [42] (also reproduced by Song et al. [36]) and Berner [45]. Most of the time series are available at a time step of 1 million years. ...
... It is relevant to note that Davis [44], analyzing different time series, namely those of Prokoph et al. [95] for temperature and Royer [43] for [CO₂], and different methodology, i.e., "detrending" the time series and examining transitions, found different results. Specifically, he found that [CO₂] is correlated weakly but negatively with linearly-detrended T proxies over the last 425 million years. ...
As a result of recent research, a new stochastic methodology of assessing causality was developed. Its application to instrumental measurements of temperature (T) and atmospheric carbon dioxide concentration ([CO₂]) over the last seven decades provided evidence for a unidirectional, potentially causal link between T as the cause and [CO₂] as the effect. Here, I refine and extend this methodology and apply it to both paleoclimatic proxy data and instrumental data of T and [CO₂]. Several proxy series, extending over the Phanerozoic or parts of it, gradually improving in accuracy and temporal resolution up to the modern period of accurate records, are compiled, paired, and analyzed. The extensive analyses made converge to the single inference that change in temperature leads, and that in carbon dioxide concentration lags. This conclusion is valid for both proxy and instrumental data in all time scales and time spans. The time scales examined begin from annual and decadal for the modern period (instrumental data) and the last two millennia (proxy data), and reach one million years for the most sparse time series for the Phanerozoic. The type of causality appears to be unidirectional, T→[CO₂], as in earlier studies. The time lags found depend on the time span and time scale and are of the same order of magnitude as the latter. These results contradict the conventional wisdom, according to which the temperature rise is caused by [CO₂] increase.
... A compilation of about 1500 discrete estimates of atmospheric CO₂ from 112 published studies covering the last 420 million years, was presented by Foster et al. [43]. Here, as shown in Figure 2, we retrieved three different time series published in the last 16 years, namely by Royer [44] (also contained in Davis [45]), Foster et al. [43] (also reproduced by Song et al. [37]) and Berner [46]. Most of the time series are available at a time step of 1 million years. ...
... It is relevant to note that Davis [45], analyzing different time series, namely those of Prokoph et al. [95] for temperature and Royer [44] for [CO₂], and different methodology, i.e. "detrending" the time series and examining transitions, found different results. Specifically, he found that [CO₂] is correlated weakly but negatively with linearly-detrended T proxies over the last 425 million years. ...
As a result of recent research, a new stochastic methodology of assessing causality was developed. Its application to instrumental measurements of temperature (T) and atmospheric carbon dioxide concentration ([CO₂]) over the last seven decades provided evidence for a unidirectional, potentially causal link between T as the cause and [CO₂] as the effect. Here we refine and extend this methodology, and apply it to both paleoclimatic proxy data and instrumental data of T and [CO₂]. Several proxy series, extending over the entire Phanerozoic or parts of it, gradually improving in accuracy and temporal resolution up to the modern period of accurate records, are compiled, paired and analyzed. The extensive analyses made converge to the single inference that change in temperature leads, and that in carbon dioxide concentration lags. This conclusion is valid for both proxy and instrumental data in all time scales and time spans. The time scales examined start from annual and decadal for the modern period (instrumental data) and the last two millennia (proxy data), and reach one million years for the most sparse time series for the entire Phanerozoic. The type of causality appears to be unidirectional, T→[CO₂], as in earlier studies. The time lags found depend on the time span and time scale and are of the same order of magnitude as the latter. These results contradict the conventional wisdom, according to which the temperature rise is caused by [CO₂] increase.
