![OwenGregorian's tweet photo. IPCC Climate Models Proven to Lack Predictive Ability | Ron Clutz, Science Matters
The recently published paper is Are Climate Model Forecasts Useful for Policy Making? by Kesten C. Green and Willie Soon.
Abstract
For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary.
The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from carbon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures.
The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The correlation between estimation sample statistical fit and forecast accuracy was -0.26. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined.
The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even
the models validated in this study may fail to improve accuracy relative to naïve forecasts due to
uncertainty over the future causal variable values. Our findings emphasize that out-of-sample
forecast errors, not statistical fit, should be used to choose between models (hypotheses).
Background
In their attempts to achieve the IPCC objective of identifying a human cause for temperature changes—specifically “global warming”—the IPCC researchers have framed the problem as one of “attributing” changes in the Earth’s temperature to the respective contributions of putative anthropogenic (“Anthro”) principally carbon dioxide emissions altering the composition of the atmosphere—and natural influences—principally aerosols from volcanic eruptions altering the composition of the atmosphere (“Volcanic”), and total solar irradiance, or TSI, variations (“Solar”).
Given the task they were set, the IPCC researchers have devoted much of their efforts into developing estimates of the Anthro variable.
The IPCC’s most recent, AR6, report (IPCC, 2021) only considered one estimate of Solar for the purpose of attribution (Matthes et al., 2017) and made no allowance for the effect of urban heat islands on the temperature measures they used (Connolly et al., 2021, 2023; Soon et al., 2023). Moreover, a study of the statistical attribution or “fingerprinting” approach used by IPCC researchers (e.g., Allen and Tett, 1999; Hasselmann, et al., 1995; Hegerl et al., 1997; Santer et al.,1995) concluded that the approach was invalid (McKitrick, 2022). The IPCC authors’ analyses failed to meet the assumptions of the method they used, and they failed to correctly implement the method.
In sum, the objective given to the IPCC researchers and the approach that they have taken suggests that plausible alternative hypotheses on the causes of terrestrial temperature changes may not have been adequately tested, as is required by the scientific method (Armstrong and Green, 2022). That concern is consistent with Armstrong and Green’s (2022) observation that government sponsorship of research can create incentives that may influence researchers’ choices of hypotheses to test and how they test them.
1.1 Alternative hypotheses on Solar
To address the first of the foregoing limitations in the IPCC attribution studies—failure to fairly test alternative TSI estimates—Connolly et al. (2021, 2023) comprehensively reviewed alternative estimates of TSI covering the 169 years from 1850 to 2018. In addition to the Matthes, et al. (2017) TSI estimates series used by the IPCC (2021)—henceforth “IPCC Solar”—Connolly et al. (2023) identified 27 alternative Solar time series.
The alternative estimates of Solar correlate quite well with the TSI used in the AR6 report—Pearson’s r values range between 0.39 and 0.97 with a median of 0.82—but the degree of TSI variation in Watts per square metre (Wm-2) differs considerably between the estimates. The ranges of the individual alternative TSI estimate series vary between 0.49 and 4.64 Wm-2, with a median range of 1.77 Wm-2, while IPCC Solar has a range of only 0.19 Wm-2.
In this study, we consider two plausible TSI reconstructions from Connolly et al. (2023). Those from Hoyt and Schatten (1993) and from Bard et al. (2000), which Connolly et al. (2023) updated to the year 20182. The former TSI record (“H1993 Solar”) was based on the so-called multiproxy—i.e., equatorial solar rotation rate, sunspot structure, the decay rate of individual sunspots, the number of sunspots without umbrae, and the length and decay rate of the 11-yr sunspot activity cycle—reconstruction of the solar irradiance history.
1.2 Alternative hypotheses on temperature estimation
The IPCC’s attribution studies do not account for the direct effects of human activities on local temperatures (heat islands)—the second weakness addressed in this study. For example, heating and cooling of building interiors, electricity generation, manufacturing, freight and transport, asphalt and concrete, and where vegetation and open water have been removed or added. Where temperature readings are taken close to such human sources of heat or absence of natural cooling, they cannot properly reflect the individual effects of human emissions of carbon dioxide, etc., that the IPCC are concerned about (their Anthro variable), the Volcanic variable, and TSI.
To address this second limitation in the IPCC attribution studies, Connolly et al. (2021, 2023) developed four alternative estimates of surface temperatures that were intended to avoid heat island effects. They were based on rural only weather station readings, sea surface temperature readings, tree-ring width measurements, and glacier length measurements. For comparison with the approach used by the IPCC, they also developed an all-land temperature estimates series for the Northern Hemisphere.
1.5 Hypotheses tested
The foregoing discussion suggests the following hypotheses, which are tested in this study.
- H1. Forecasts from causal models will [will not] be usefully more accurate than forecasts from a naïve no-change model.
- H2. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] have greater predictive validity.
- H3. The statistical fit of the models (adjusted-R2) will not [will] be substantively positively related to their predictive validity.
- H4. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] be more reliable.
Findings
3.1 Predictive validity of causal models versus naïve model [H1]
Forecast errors were larger than the benchmark errors (UMBRAE) for the IPCC Anthro models AVL and AVSL estimated with data from 1850 to 1949 and from 1850 to 1969, and for the AVR and AVSR models estimated with data from 1850 to 1899, 1850 to 1949, and 1850 to 1969. The anthropogenic warming models showed predictive validity relative the naïve model (UMBRAE less than 1.0) for only three of the eight combinations of forecast variable and estimation sample period.
3.2 Predictive validity of independent versus IPCC models [H2]
The MdAEs (median absolute error) of the forecasts from the models with IPCC’s anthropogenic and volcanic series as causal variables (AVL and AVR) and from the models that also included IPCC’s solar series (AVSL and AVSR) were greater than 1°C (roughly 2°F) for five of the eight combinations tested. The MdAEs of the forecasts from the models with B2000 solar and the volcanic series as causal variables (SBVL and SBVR) were less than 0.55°C (1°F) for all eight of the estimation periods used and temperature series being forecast combinations and for seven of the eight in the case of the models with H1993 as the solar variable (SHVL and SHVR).
3.3 Relationship between predictive validity and statistical fit of models [H3]
The correlations (sign-reversed Pearson’s r) between the accuracy of out-of-sample forecasts, as measured by UMBRAE (an error measure, hence the sign reversal), and the statistical fit of the models to the estimation data (adjusted-R2) for the causal models tested were large and negative for six (6) of the eight (8) combinations of estimation period (1850 to 1899, 1949, 1969, and 1999) used—and hence maximum forecast horizon of 119, 69, 49, and 19 years, respectively—and temperature series (NH Land and NH Rural) forecast.
3.4 Reliability of independent versus IPCC models [H4]
Charts of the results of Test 2 are presented in Figure 2 and are discussed below.
The independent solar models—SBVL and SHVL, and SBVR and SHVR—perform largely as one
would expect of causal models when forecasting using known values of the causal variables.
In the case of the AVR and AVSR models—forecasting the rural land temperatures, on the right of Figure 2—the MdAEs decreased rapidly from roughly 17 times the corresponding naïve forecast errors to beat the naïve MdAE when the 76th observation (1925) was added to the estimation samples. After that observation was added, the MdAEs for the AVR and AVSR model forecasts increased rapidly with each extra observation then stayed high before rapidly declining again after the 116th observation (1965) was added to the estimation samples.
When a model of causal relationships is estimated from empirical data on valid causal variables reliably measured, one would expect forecast errors to get smaller as more observations are used in the estimation of the model’s parameters. That is what the charts in Figure 2 show in the case of the naïve benchmark model forecasts and, broadly, what can be seen in the case of the independent models SBVL, SHVL, SBVR, and SHVR, but is not seen in the case of the models using the IPCC variables: AVL, AVSL, AVR, and AVSR.
The errors of the Anthro models’ forecast errors explode well beyond 1 °C and the benchmark model errors for forecast years beyond the mid-1970s, with puzzling exceptions. Namely, forecasts from Anthro models estimated from the largest sample size in the chart—1850 to 1999—and from models estimated from the smallest sample—1850 to 1899—forecasting All Land temperatures. In those cases, involving three of the eight charts, the Anthro model errors are less than the median historical temperature benchmark model errors, and mostly less than the errors of the independent models in later years.
The explosion in Anthro model errors from the 1970s is more extreme for models estimated to forecast Rural Land temperatures. Moreover, for the models estimated using only 1850 to 1899 data, errors are larger than those of the benchmark and independent models from 1920 and, prior to 1970, without any obvious pattern.
5. Conclusions
The IPCC’s models of anthropogenic climate change lack predictive validity. The IPCC models’ forecast errors were greater for most estimation samples —often many times greater—than those from a benchmark model that simply predicts that future years’ temperatures will be the same as the historical median. The size of the forecast errors and unreliability of the models’ forecasts in response to additional observations in the estimation sample implies that the anthropogenic models fail to realistically capture and represent the causes of Earth’s surface temperature changes. In practice, the IPCC models’ relative forecast errors would be still greater due to the uncertainty in forecasting the models’ causal variables, particularly Volcanic and IPCC Solar.
The independent solar models of climate change—which did not include a variable representing the IPCC postulated anthropogenic influence—do have predictive validity. The models reduced errors of forecasts for the years 2000 to 2018 relative to the benchmark errors for all, and all but one of 101 estimation samples tested for each of the two models. One of the models (B2000 Solar) reduced errors by more than 75 percent for forecasts from models estimated from 35 of the samples—a particularly impressive improvement given that the benchmark errors were no greater than 1 °C for all but one of the estimation samples.
The independent solar models provide realistic representations of the causal relationships with surface temperatures. The question of whether the independent solar variables can be forecast with sufficient accuracy to improve on the benchmark model forecasts in practice, however, remains relevant. All in all, and contra to the IPCC reports, there is insufficient evidential basis for the use of carbon dioxide, et cetera, emissions—taken together, the IPCC’s Anthro—as climate policy variables.
Read more:
https://t.co/cv3iFjOLTs](https://pbs.twimg.com/media/GrodWCRXEAAabgV.png)
![OwenGregorian's tweet photo. IPCC Climate Models Proven to Lack Predictive Ability | Ron Clutz, Science Matters
The recently published paper is Are Climate Model Forecasts Useful for Policy Making? by Kesten C. Green and Willie Soon.
Abstract
For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary.
The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from carbon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures.
The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The correlation between estimation sample statistical fit and forecast accuracy was -0.26. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined.
The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even
the models validated in this study may fail to improve accuracy relative to naïve forecasts due to
uncertainty over the future causal variable values. Our findings emphasize that out-of-sample
forecast errors, not statistical fit, should be used to choose between models (hypotheses).
Background
In their attempts to achieve the IPCC objective of identifying a human cause for temperature changes—specifically “global warming”—the IPCC researchers have framed the problem as one of “attributing” changes in the Earth’s temperature to the respective contributions of putative anthropogenic (“Anthro”) principally carbon dioxide emissions altering the composition of the atmosphere—and natural influences—principally aerosols from volcanic eruptions altering the composition of the atmosphere (“Volcanic”), and total solar irradiance, or TSI, variations (“Solar”).
Given the task they were set, the IPCC researchers have devoted much of their efforts into developing estimates of the Anthro variable.
The IPCC’s most recent, AR6, report (IPCC, 2021) only considered one estimate of Solar for the purpose of attribution (Matthes et al., 2017) and made no allowance for the effect of urban heat islands on the temperature measures they used (Connolly et al., 2021, 2023; Soon et al., 2023). Moreover, a study of the statistical attribution or “fingerprinting” approach used by IPCC researchers (e.g., Allen and Tett, 1999; Hasselmann, et al., 1995; Hegerl et al., 1997; Santer et al.,1995) concluded that the approach was invalid (McKitrick, 2022). The IPCC authors’ analyses failed to meet the assumptions of the method they used, and they failed to correctly implement the method.
In sum, the objective given to the IPCC researchers and the approach that they have taken suggests that plausible alternative hypotheses on the causes of terrestrial temperature changes may not have been adequately tested, as is required by the scientific method (Armstrong and Green, 2022). That concern is consistent with Armstrong and Green’s (2022) observation that government sponsorship of research can create incentives that may influence researchers’ choices of hypotheses to test and how they test them.
1.1 Alternative hypotheses on Solar
To address the first of the foregoing limitations in the IPCC attribution studies—failure to fairly test alternative TSI estimates—Connolly et al. (2021, 2023) comprehensively reviewed alternative estimates of TSI covering the 169 years from 1850 to 2018. In addition to the Matthes, et al. (2017) TSI estimates series used by the IPCC (2021)—henceforth “IPCC Solar”—Connolly et al. (2023) identified 27 alternative Solar time series.
The alternative estimates of Solar correlate quite well with the TSI used in the AR6 report—Pearson’s r values range between 0.39 and 0.97 with a median of 0.82—but the degree of TSI variation in Watts per square metre (Wm-2) differs considerably between the estimates. The ranges of the individual alternative TSI estimate series vary between 0.49 and 4.64 Wm-2, with a median range of 1.77 Wm-2, while IPCC Solar has a range of only 0.19 Wm-2.
In this study, we consider two plausible TSI reconstructions from Connolly et al. (2023). Those from Hoyt and Schatten (1993) and from Bard et al. (2000), which Connolly et al. (2023) updated to the year 20182. The former TSI record (“H1993 Solar”) was based on the so-called multiproxy—i.e., equatorial solar rotation rate, sunspot structure, the decay rate of individual sunspots, the number of sunspots without umbrae, and the length and decay rate of the 11-yr sunspot activity cycle—reconstruction of the solar irradiance history.
1.2 Alternative hypotheses on temperature estimation
The IPCC’s attribution studies do not account for the direct effects of human activities on local temperatures (heat islands)—the second weakness addressed in this study. For example, heating and cooling of building interiors, electricity generation, manufacturing, freight and transport, asphalt and concrete, and where vegetation and open water have been removed or added. Where temperature readings are taken close to such human sources of heat or absence of natural cooling, they cannot properly reflect the individual effects of human emissions of carbon dioxide, etc., that the IPCC are concerned about (their Anthro variable), the Volcanic variable, and TSI.
To address this second limitation in the IPCC attribution studies, Connolly et al. (2021, 2023) developed four alternative estimates of surface temperatures that were intended to avoid heat island effects. They were based on rural only weather station readings, sea surface temperature readings, tree-ring width measurements, and glacier length measurements. For comparison with the approach used by the IPCC, they also developed an all-land temperature estimates series for the Northern Hemisphere.
1.5 Hypotheses tested
The foregoing discussion suggests the following hypotheses, which are tested in this study.
- H1. Forecasts from causal models will [will not] be usefully more accurate than forecasts from a naïve no-change model.
- H2. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] have greater predictive validity.
- H3. The statistical fit of the models (adjusted-R2) will not [will] be substantively positively related to their predictive validity.
- H4. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] be more reliable.
Findings
3.1 Predictive validity of causal models versus naïve model [H1]
Forecast errors were larger than the benchmark errors (UMBRAE) for the IPCC Anthro models AVL and AVSL estimated with data from 1850 to 1949 and from 1850 to 1969, and for the AVR and AVSR models estimated with data from 1850 to 1899, 1850 to 1949, and 1850 to 1969. The anthropogenic warming models showed predictive validity relative the naïve model (UMBRAE less than 1.0) for only three of the eight combinations of forecast variable and estimation sample period.
3.2 Predictive validity of independent versus IPCC models [H2]
The MdAEs (median absolute error) of the forecasts from the models with IPCC’s anthropogenic and volcanic series as causal variables (AVL and AVR) and from the models that also included IPCC’s solar series (AVSL and AVSR) were greater than 1°C (roughly 2°F) for five of the eight combinations tested. The MdAEs of the forecasts from the models with B2000 solar and the volcanic series as causal variables (SBVL and SBVR) were less than 0.55°C (1°F) for all eight of the estimation periods used and temperature series being forecast combinations and for seven of the eight in the case of the models with H1993 as the solar variable (SHVL and SHVR).
3.3 Relationship between predictive validity and statistical fit of models [H3]
The correlations (sign-reversed Pearson’s r) between the accuracy of out-of-sample forecasts, as measured by UMBRAE (an error measure, hence the sign reversal), and the statistical fit of the models to the estimation data (adjusted-R2) for the causal models tested were large and negative for six (6) of the eight (8) combinations of estimation period (1850 to 1899, 1949, 1969, and 1999) used—and hence maximum forecast horizon of 119, 69, 49, and 19 years, respectively—and temperature series (NH Land and NH Rural) forecast.
3.4 Reliability of independent versus IPCC models [H4]
Charts of the results of Test 2 are presented in Figure 2 and are discussed below.
The independent solar models—SBVL and SHVL, and SBVR and SHVR—perform largely as one
would expect of causal models when forecasting using known values of the causal variables.
In the case of the AVR and AVSR models—forecasting the rural land temperatures, on the right of Figure 2—the MdAEs decreased rapidly from roughly 17 times the corresponding naïve forecast errors to beat the naïve MdAE when the 76th observation (1925) was added to the estimation samples. After that observation was added, the MdAEs for the AVR and AVSR model forecasts increased rapidly with each extra observation then stayed high before rapidly declining again after the 116th observation (1965) was added to the estimation samples.
When a model of causal relationships is estimated from empirical data on valid causal variables reliably measured, one would expect forecast errors to get smaller as more observations are used in the estimation of the model’s parameters. That is what the charts in Figure 2 show in the case of the naïve benchmark model forecasts and, broadly, what can be seen in the case of the independent models SBVL, SHVL, SBVR, and SHVR, but is not seen in the case of the models using the IPCC variables: AVL, AVSL, AVR, and AVSR.
The errors of the Anthro models’ forecast errors explode well beyond 1 °C and the benchmark model errors for forecast years beyond the mid-1970s, with puzzling exceptions. Namely, forecasts from Anthro models estimated from the largest sample size in the chart—1850 to 1999—and from models estimated from the smallest sample—1850 to 1899—forecasting All Land temperatures. In those cases, involving three of the eight charts, the Anthro model errors are less than the median historical temperature benchmark model errors, and mostly less than the errors of the independent models in later years.
The explosion in Anthro model errors from the 1970s is more extreme for models estimated to forecast Rural Land temperatures. Moreover, for the models estimated using only 1850 to 1899 data, errors are larger than those of the benchmark and independent models from 1920 and, prior to 1970, without any obvious pattern.
5. Conclusions
The IPCC’s models of anthropogenic climate change lack predictive validity. The IPCC models’ forecast errors were greater for most estimation samples —often many times greater—than those from a benchmark model that simply predicts that future years’ temperatures will be the same as the historical median. The size of the forecast errors and unreliability of the models’ forecasts in response to additional observations in the estimation sample implies that the anthropogenic models fail to realistically capture and represent the causes of Earth’s surface temperature changes. In practice, the IPCC models’ relative forecast errors would be still greater due to the uncertainty in forecasting the models’ causal variables, particularly Volcanic and IPCC Solar.
The independent solar models of climate change—which did not include a variable representing the IPCC postulated anthropogenic influence—do have predictive validity. The models reduced errors of forecasts for the years 2000 to 2018 relative to the benchmark errors for all, and all but one of 101 estimation samples tested for each of the two models. One of the models (B2000 Solar) reduced errors by more than 75 percent for forecasts from models estimated from 35 of the samples—a particularly impressive improvement given that the benchmark errors were no greater than 1 °C for all but one of the estimation samples.
The independent solar models provide realistic representations of the causal relationships with surface temperatures. The question of whether the independent solar variables can be forecast with sufficient accuracy to improve on the benchmark model forecasts in practice, however, remains relevant. All in all, and contra to the IPCC reports, there is insufficient evidential basis for the use of carbon dioxide, et cetera, emissions—taken together, the IPCC’s Anthro—as climate policy variables.
Read more:
https://t.co/cv3iFjOLTs](https://pbs.twimg.com/media/GrodGytXYAEtfKa.png)
![OwenGregorian's tweet photo. IPCC Climate Models Proven to Lack Predictive Ability | Ron Clutz, Science Matters
The recently published paper is Are Climate Model Forecasts Useful for Policy Making? by Kesten C. Green and Willie Soon.
Abstract
For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary.
The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from carbon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures.
The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The correlation between estimation sample statistical fit and forecast accuracy was -0.26. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined.
The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even
the models validated in this study may fail to improve accuracy relative to naïve forecasts due to
uncertainty over the future causal variable values. Our findings emphasize that out-of-sample
forecast errors, not statistical fit, should be used to choose between models (hypotheses).
Background
In their attempts to achieve the IPCC objective of identifying a human cause for temperature changes—specifically “global warming”—the IPCC researchers have framed the problem as one of “attributing” changes in the Earth’s temperature to the respective contributions of putative anthropogenic (“Anthro”) principally carbon dioxide emissions altering the composition of the atmosphere—and natural influences—principally aerosols from volcanic eruptions altering the composition of the atmosphere (“Volcanic”), and total solar irradiance, or TSI, variations (“Solar”).
Given the task they were set, the IPCC researchers have devoted much of their efforts into developing estimates of the Anthro variable.
The IPCC’s most recent, AR6, report (IPCC, 2021) only considered one estimate of Solar for the purpose of attribution (Matthes et al., 2017) and made no allowance for the effect of urban heat islands on the temperature measures they used (Connolly et al., 2021, 2023; Soon et al., 2023). Moreover, a study of the statistical attribution or “fingerprinting” approach used by IPCC researchers (e.g., Allen and Tett, 1999; Hasselmann, et al., 1995; Hegerl et al., 1997; Santer et al.,1995) concluded that the approach was invalid (McKitrick, 2022). The IPCC authors’ analyses failed to meet the assumptions of the method they used, and they failed to correctly implement the method.
In sum, the objective given to the IPCC researchers and the approach that they have taken suggests that plausible alternative hypotheses on the causes of terrestrial temperature changes may not have been adequately tested, as is required by the scientific method (Armstrong and Green, 2022). That concern is consistent with Armstrong and Green’s (2022) observation that government sponsorship of research can create incentives that may influence researchers’ choices of hypotheses to test and how they test them.
1.1 Alternative hypotheses on Solar
To address the first of the foregoing limitations in the IPCC attribution studies—failure to fairly test alternative TSI estimates—Connolly et al. (2021, 2023) comprehensively reviewed alternative estimates of TSI covering the 169 years from 1850 to 2018. In addition to the Matthes, et al. (2017) TSI estimates series used by the IPCC (2021)—henceforth “IPCC Solar”—Connolly et al. (2023) identified 27 alternative Solar time series.
The alternative estimates of Solar correlate quite well with the TSI used in the AR6 report—Pearson’s r values range between 0.39 and 0.97 with a median of 0.82—but the degree of TSI variation in Watts per square metre (Wm-2) differs considerably between the estimates. The ranges of the individual alternative TSI estimate series vary between 0.49 and 4.64 Wm-2, with a median range of 1.77 Wm-2, while IPCC Solar has a range of only 0.19 Wm-2.
In this study, we consider two plausible TSI reconstructions from Connolly et al. (2023). Those from Hoyt and Schatten (1993) and from Bard et al. (2000), which Connolly et al. (2023) updated to the year 20182. The former TSI record (“H1993 Solar”) was based on the so-called multiproxy—i.e., equatorial solar rotation rate, sunspot structure, the decay rate of individual sunspots, the number of sunspots without umbrae, and the length and decay rate of the 11-yr sunspot activity cycle—reconstruction of the solar irradiance history.
1.2 Alternative hypotheses on temperature estimation
The IPCC’s attribution studies do not account for the direct effects of human activities on local temperatures (heat islands)—the second weakness addressed in this study. For example, heating and cooling of building interiors, electricity generation, manufacturing, freight and transport, asphalt and concrete, and where vegetation and open water have been removed or added. Where temperature readings are taken close to such human sources of heat or absence of natural cooling, they cannot properly reflect the individual effects of human emissions of carbon dioxide, etc., that the IPCC are concerned about (their Anthro variable), the Volcanic variable, and TSI.
To address this second limitation in the IPCC attribution studies, Connolly et al. (2021, 2023) developed four alternative estimates of surface temperatures that were intended to avoid heat island effects. They were based on rural only weather station readings, sea surface temperature readings, tree-ring width measurements, and glacier length measurements. For comparison with the approach used by the IPCC, they also developed an all-land temperature estimates series for the Northern Hemisphere.
1.5 Hypotheses tested
The foregoing discussion suggests the following hypotheses, which are tested in this study.
- H1. Forecasts from causal models will [will not] be usefully more accurate than forecasts from a naïve no-change model.
- H2. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] have greater predictive validity.
- H3. The statistical fit of the models (adjusted-R2) will not [will] be substantively positively related to their predictive validity.
- H4. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] be more reliable.
Findings
3.1 Predictive validity of causal models versus naïve model [H1]
Forecast errors were larger than the benchmark errors (UMBRAE) for the IPCC Anthro models AVL and AVSL estimated with data from 1850 to 1949 and from 1850 to 1969, and for the AVR and AVSR models estimated with data from 1850 to 1899, 1850 to 1949, and 1850 to 1969. The anthropogenic warming models showed predictive validity relative the naïve model (UMBRAE less than 1.0) for only three of the eight combinations of forecast variable and estimation sample period.
3.2 Predictive validity of independent versus IPCC models [H2]
The MdAEs (median absolute error) of the forecasts from the models with IPCC’s anthropogenic and volcanic series as causal variables (AVL and AVR) and from the models that also included IPCC’s solar series (AVSL and AVSR) were greater than 1°C (roughly 2°F) for five of the eight combinations tested. The MdAEs of the forecasts from the models with B2000 solar and the volcanic series as causal variables (SBVL and SBVR) were less than 0.55°C (1°F) for all eight of the estimation periods used and temperature series being forecast combinations and for seven of the eight in the case of the models with H1993 as the solar variable (SHVL and SHVR).
3.3 Relationship between predictive validity and statistical fit of models [H3]
The correlations (sign-reversed Pearson’s r) between the accuracy of out-of-sample forecasts, as measured by UMBRAE (an error measure, hence the sign reversal), and the statistical fit of the models to the estimation data (adjusted-R2) for the causal models tested were large and negative for six (6) of the eight (8) combinations of estimation period (1850 to 1899, 1949, 1969, and 1999) used—and hence maximum forecast horizon of 119, 69, 49, and 19 years, respectively—and temperature series (NH Land and NH Rural) forecast.
3.4 Reliability of independent versus IPCC models [H4]
Charts of the results of Test 2 are presented in Figure 2 and are discussed below.
The independent solar models—SBVL and SHVL, and SBVR and SHVR—perform largely as one
would expect of causal models when forecasting using known values of the causal variables.
In the case of the AVR and AVSR models—forecasting the rural land temperatures, on the right of Figure 2—the MdAEs decreased rapidly from roughly 17 times the corresponding naïve forecast errors to beat the naïve MdAE when the 76th observation (1925) was added to the estimation samples. After that observation was added, the MdAEs for the AVR and AVSR model forecasts increased rapidly with each extra observation then stayed high before rapidly declining again after the 116th observation (1965) was added to the estimation samples.
When a model of causal relationships is estimated from empirical data on valid causal variables reliably measured, one would expect forecast errors to get smaller as more observations are used in the estimation of the model’s parameters. That is what the charts in Figure 2 show in the case of the naïve benchmark model forecasts and, broadly, what can be seen in the case of the independent models SBVL, SHVL, SBVR, and SHVR, but is not seen in the case of the models using the IPCC variables: AVL, AVSL, AVR, and AVSR.
The errors of the Anthro models’ forecast errors explode well beyond 1 °C and the benchmark model errors for forecast years beyond the mid-1970s, with puzzling exceptions. Namely, forecasts from Anthro models estimated from the largest sample size in the chart—1850 to 1999—and from models estimated from the smallest sample—1850 to 1899—forecasting All Land temperatures. In those cases, involving three of the eight charts, the Anthro model errors are less than the median historical temperature benchmark model errors, and mostly less than the errors of the independent models in later years.
The explosion in Anthro model errors from the 1970s is more extreme for models estimated to forecast Rural Land temperatures. Moreover, for the models estimated using only 1850 to 1899 data, errors are larger than those of the benchmark and independent models from 1920 and, prior to 1970, without any obvious pattern.
5. Conclusions
The IPCC’s models of anthropogenic climate change lack predictive validity. The IPCC models’ forecast errors were greater for most estimation samples —often many times greater—than those from a benchmark model that simply predicts that future years’ temperatures will be the same as the historical median. The size of the forecast errors and unreliability of the models’ forecasts in response to additional observations in the estimation sample implies that the anthropogenic models fail to realistically capture and represent the causes of Earth’s surface temperature changes. In practice, the IPCC models’ relative forecast errors would be still greater due to the uncertainty in forecasting the models’ causal variables, particularly Volcanic and IPCC Solar.
The independent solar models of climate change—which did not include a variable representing the IPCC postulated anthropogenic influence—do have predictive validity. The models reduced errors of forecasts for the years 2000 to 2018 relative to the benchmark errors for all, and all but one of 101 estimation samples tested for each of the two models. One of the models (B2000 Solar) reduced errors by more than 75 percent for forecasts from models estimated from 35 of the samples—a particularly impressive improvement given that the benchmark errors were no greater than 1 °C for all but one of the estimation samples.
The independent solar models provide realistic representations of the causal relationships with surface temperatures. The question of whether the independent solar variables can be forecast with sufficient accuracy to improve on the benchmark model forecasts in practice, however, remains relevant. All in all, and contra to the IPCC reports, there is insufficient evidential basis for the use of carbon dioxide, et cetera, emissions—taken together, the IPCC’s Anthro—as climate policy variables.
Read more:
https://t.co/cv3iFjOLTs](https://pbs.twimg.com/media/Groc0qFW0AE7f6h.png)
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@morgandevi @ArchRose90 Not uncommon to see people masked everywhere in London (unfortunately). Police turn a blind eye. Soft
Find out just how hard you can cringe by watching GB News dickhead, Martin Daubney, saluting cars in the hope that Trump is in one of them.
Because homogonous populations are more likely to maintain a cohesive culture and build social structures that resist the centralization of state power
Particular peoples with deeply ingrained customs and religions are less likely to bend to the state's commands when they conflict with those beliefs
They are also more likely to be rooted in communities with the social and economic capital to be self-reliant and are therefore more difficult to control through soft power
Kulaks are a problem for totalitarian regimes so the elites import a foreign class that is generally poor, dependent, and too diverse to resist the government
They gain their loyalty by promising to strip the kulaks of their wealth and distribute it to the new client class
It eliminates the middle-class as a barrier to their power and wins them reliable voter base of slave labor that will ensure they remain in charge "democratically"
Who to follow
@wikipenny1 agree, and how about charge £10 for each appointment (for the majority)…
IPCC Climate Models Proven to Lack Predictive Ability | Ron Clutz, Science Matters
The recently published paper is Are Climate Model Forecasts Useful for Policy Making? by Kesten C. Green and Willie Soon.
Abstract
For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary.
The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from carbon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures.
The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The correlation between estimation sample statistical fit and forecast accuracy was -0.26. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined.
The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even
the models validated in this study may fail to improve accuracy relative to naïve forecasts due to
uncertainty over the future causal variable values. Our findings emphasize that out-of-sample
forecast errors, not statistical fit, should be used to choose between models (hypotheses).
Background
In their attempts to achieve the IPCC objective of identifying a human cause for temperature changes—specifically “global warming”—the IPCC researchers have framed the problem as one of “attributing” changes in the Earth’s temperature to the respective contributions of putative anthropogenic (“Anthro”) principally carbon dioxide emissions altering the composition of the atmosphere—and natural influences—principally aerosols from volcanic eruptions altering the composition of the atmosphere (“Volcanic”), and total solar irradiance, or TSI, variations (“Solar”).
Given the task they were set, the IPCC researchers have devoted much of their efforts into developing estimates of the Anthro variable.
The IPCC’s most recent, AR6, report (IPCC, 2021) only considered one estimate of Solar for the purpose of attribution (Matthes et al., 2017) and made no allowance for the effect of urban heat islands on the temperature measures they used (Connolly et al., 2021, 2023; Soon et al., 2023). Moreover, a study of the statistical attribution or “fingerprinting” approach used by IPCC researchers (e.g., Allen and Tett, 1999; Hasselmann, et al., 1995; Hegerl et al., 1997; Santer et al.,1995) concluded that the approach was invalid (McKitrick, 2022). The IPCC authors’ analyses failed to meet the assumptions of the method they used, and they failed to correctly implement the method.
In sum, the objective given to the IPCC researchers and the approach that they have taken suggests that plausible alternative hypotheses on the causes of terrestrial temperature changes may not have been adequately tested, as is required by the scientific method (Armstrong and Green, 2022). That concern is consistent with Armstrong and Green’s (2022) observation that government sponsorship of research can create incentives that may influence researchers’ choices of hypotheses to test and how they test them.
1.1 Alternative hypotheses on Solar
To address the first of the foregoing limitations in the IPCC attribution studies—failure to fairly test alternative TSI estimates—Connolly et al. (2021, 2023) comprehensively reviewed alternative estimates of TSI covering the 169 years from 1850 to 2018. In addition to the Matthes, et al. (2017) TSI estimates series used by the IPCC (2021)—henceforth “IPCC Solar”—Connolly et al. (2023) identified 27 alternative Solar time series.
The alternative estimates of Solar correlate quite well with the TSI used in the AR6 report—Pearson’s r values range between 0.39 and 0.97 with a median of 0.82—but the degree of TSI variation in Watts per square metre (Wm-2) differs considerably between the estimates. The ranges of the individual alternative TSI estimate series vary between 0.49 and 4.64 Wm-2, with a median range of 1.77 Wm-2, while IPCC Solar has a range of only 0.19 Wm-2.
In this study, we consider two plausible TSI reconstructions from Connolly et al. (2023). Those from Hoyt and Schatten (1993) and from Bard et al. (2000), which Connolly et al. (2023) updated to the year 20182. The former TSI record (“H1993 Solar”) was based on the so-called multiproxy—i.e., equatorial solar rotation rate, sunspot structure, the decay rate of individual sunspots, the number of sunspots without umbrae, and the length and decay rate of the 11-yr sunspot activity cycle—reconstruction of the solar irradiance history.
1.2 Alternative hypotheses on temperature estimation
The IPCC’s attribution studies do not account for the direct effects of human activities on local temperatures (heat islands)—the second weakness addressed in this study. For example, heating and cooling of building interiors, electricity generation, manufacturing, freight and transport, asphalt and concrete, and where vegetation and open water have been removed or added. Where temperature readings are taken close to such human sources of heat or absence of natural cooling, they cannot properly reflect the individual effects of human emissions of carbon dioxide, etc., that the IPCC are concerned about (their Anthro variable), the Volcanic variable, and TSI.
To address this second limitation in the IPCC attribution studies, Connolly et al. (2021, 2023) developed four alternative estimates of surface temperatures that were intended to avoid heat island effects. They were based on rural only weather station readings, sea surface temperature readings, tree-ring width measurements, and glacier length measurements. For comparison with the approach used by the IPCC, they also developed an all-land temperature estimates series for the Northern Hemisphere.
1.5 Hypotheses tested
The foregoing discussion suggests the following hypotheses, which are tested in this study.
- H1. Forecasts from causal models will [will not] be usefully more accurate than forecasts from a naïve no-change model.
- H2. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] have greater predictive validity.
- H3. The statistical fit of the models (adjusted-R2) will not [will] be substantively positively related to their predictive validity.
- H4. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] be more reliable.
Findings
3.1 Predictive validity of causal models versus naïve model [H1]
Forecast errors were larger than the benchmark errors (UMBRAE) for the IPCC Anthro models AVL and AVSL estimated with data from 1850 to 1949 and from 1850 to 1969, and for the AVR and AVSR models estimated with data from 1850 to 1899, 1850 to 1949, and 1850 to 1969. The anthropogenic warming models showed predictive validity relative the naïve model (UMBRAE less than 1.0) for only three of the eight combinations of forecast variable and estimation sample period.
3.2 Predictive validity of independent versus IPCC models [H2]
The MdAEs (median absolute error) of the forecasts from the models with IPCC’s anthropogenic and volcanic series as causal variables (AVL and AVR) and from the models that also included IPCC’s solar series (AVSL and AVSR) were greater than 1°C (roughly 2°F) for five of the eight combinations tested. The MdAEs of the forecasts from the models with B2000 solar and the volcanic series as causal variables (SBVL and SBVR) were less than 0.55°C (1°F) for all eight of the estimation periods used and temperature series being forecast combinations and for seven of the eight in the case of the models with H1993 as the solar variable (SHVL and SHVR).
3.3 Relationship between predictive validity and statistical fit of models [H3]
The correlations (sign-reversed Pearson’s r) between the accuracy of out-of-sample forecasts, as measured by UMBRAE (an error measure, hence the sign reversal), and the statistical fit of the models to the estimation data (adjusted-R2) for the causal models tested were large and negative for six (6) of the eight (8) combinations of estimation period (1850 to 1899, 1949, 1969, and 1999) used—and hence maximum forecast horizon of 119, 69, 49, and 19 years, respectively—and temperature series (NH Land and NH Rural) forecast.
3.4 Reliability of independent versus IPCC models [H4]
Charts of the results of Test 2 are presented in Figure 2 and are discussed below.
The independent solar models—SBVL and SHVL, and SBVR and SHVR—perform largely as one
would expect of causal models when forecasting using known values of the causal variables.
In the case of the AVR and AVSR models—forecasting the rural land temperatures, on the right of Figure 2—the MdAEs decreased rapidly from roughly 17 times the corresponding naïve forecast errors to beat the naïve MdAE when the 76th observation (1925) was added to the estimation samples. After that observation was added, the MdAEs for the AVR and AVSR model forecasts increased rapidly with each extra observation then stayed high before rapidly declining again after the 116th observation (1965) was added to the estimation samples.
When a model of causal relationships is estimated from empirical data on valid causal variables reliably measured, one would expect forecast errors to get smaller as more observations are used in the estimation of the model’s parameters. That is what the charts in Figure 2 show in the case of the naïve benchmark model forecasts and, broadly, what can be seen in the case of the independent models SBVL, SHVL, SBVR, and SHVR, but is not seen in the case of the models using the IPCC variables: AVL, AVSL, AVR, and AVSR.
The errors of the Anthro models’ forecast errors explode well beyond 1 °C and the benchmark model errors for forecast years beyond the mid-1970s, with puzzling exceptions. Namely, forecasts from Anthro models estimated from the largest sample size in the chart—1850 to 1999—and from models estimated from the smallest sample—1850 to 1899—forecasting All Land temperatures. In those cases, involving three of the eight charts, the Anthro model errors are less than the median historical temperature benchmark model errors, and mostly less than the errors of the independent models in later years.
The explosion in Anthro model errors from the 1970s is more extreme for models estimated to forecast Rural Land temperatures. Moreover, for the models estimated using only 1850 to 1899 data, errors are larger than those of the benchmark and independent models from 1920 and, prior to 1970, without any obvious pattern.
5. Conclusions
The IPCC’s models of anthropogenic climate change lack predictive validity. The IPCC models’ forecast errors were greater for most estimation samples —often many times greater—than those from a benchmark model that simply predicts that future years’ temperatures will be the same as the historical median. The size of the forecast errors and unreliability of the models’ forecasts in response to additional observations in the estimation sample implies that the anthropogenic models fail to realistically capture and represent the causes of Earth’s surface temperature changes. In practice, the IPCC models’ relative forecast errors would be still greater due to the uncertainty in forecasting the models’ causal variables, particularly Volcanic and IPCC Solar.
The independent solar models of climate change—which did not include a variable representing the IPCC postulated anthropogenic influence—do have predictive validity. The models reduced errors of forecasts for the years 2000 to 2018 relative to the benchmark errors for all, and all but one of 101 estimation samples tested for each of the two models. One of the models (B2000 Solar) reduced errors by more than 75 percent for forecasts from models estimated from 35 of the samples—a particularly impressive improvement given that the benchmark errors were no greater than 1 °C for all but one of the estimation samples.
The independent solar models provide realistic representations of the causal relationships with surface temperatures. The question of whether the independent solar variables can be forecast with sufficient accuracy to improve on the benchmark model forecasts in practice, however, remains relevant. All in all, and contra to the IPCC reports, there is insufficient evidential basis for the use of carbon dioxide, et cetera, emissions—taken together, the IPCC’s Anthro—as climate policy variables.
Read more:
https://t.co/cv3iFjOLTs
![OwenGregorian's tweet photo. IPCC Climate Models Proven to Lack Predictive Ability | Ron Clutz, Science Matters
The recently published paper is Are Climate Model Forecasts Useful for Policy Making? by Kesten C. Green and Willie Soon.
Abstract
For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary.
The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from carbon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures.
The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The correlation between estimation sample statistical fit and forecast accuracy was -0.26. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined.
The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even
the models validated in this study may fail to improve accuracy relative to naïve forecasts due to
uncertainty over the future causal variable values. Our findings emphasize that out-of-sample
forecast errors, not statistical fit, should be used to choose between models (hypotheses).
Background
In their attempts to achieve the IPCC objective of identifying a human cause for temperature changes—specifically “global warming”—the IPCC researchers have framed the problem as one of “attributing” changes in the Earth’s temperature to the respective contributions of putative anthropogenic (“Anthro”) principally carbon dioxide emissions altering the composition of the atmosphere—and natural influences—principally aerosols from volcanic eruptions altering the composition of the atmosphere (“Volcanic”), and total solar irradiance, or TSI, variations (“Solar”).
Given the task they were set, the IPCC researchers have devoted much of their efforts into developing estimates of the Anthro variable.
The IPCC’s most recent, AR6, report (IPCC, 2021) only considered one estimate of Solar for the purpose of attribution (Matthes et al., 2017) and made no allowance for the effect of urban heat islands on the temperature measures they used (Connolly et al., 2021, 2023; Soon et al., 2023). Moreover, a study of the statistical attribution or “fingerprinting” approach used by IPCC researchers (e.g., Allen and Tett, 1999; Hasselmann, et al., 1995; Hegerl et al., 1997; Santer et al.,1995) concluded that the approach was invalid (McKitrick, 2022). The IPCC authors’ analyses failed to meet the assumptions of the method they used, and they failed to correctly implement the method.
In sum, the objective given to the IPCC researchers and the approach that they have taken suggests that plausible alternative hypotheses on the causes of terrestrial temperature changes may not have been adequately tested, as is required by the scientific method (Armstrong and Green, 2022). That concern is consistent with Armstrong and Green’s (2022) observation that government sponsorship of research can create incentives that may influence researchers’ choices of hypotheses to test and how they test them.
1.1 Alternative hypotheses on Solar
To address the first of the foregoing limitations in the IPCC attribution studies—failure to fairly test alternative TSI estimates—Connolly et al. (2021, 2023) comprehensively reviewed alternative estimates of TSI covering the 169 years from 1850 to 2018. In addition to the Matthes, et al. (2017) TSI estimates series used by the IPCC (2021)—henceforth “IPCC Solar”—Connolly et al. (2023) identified 27 alternative Solar time series.
The alternative estimates of Solar correlate quite well with the TSI used in the AR6 report—Pearson’s r values range between 0.39 and 0.97 with a median of 0.82—but the degree of TSI variation in Watts per square metre (Wm-2) differs considerably between the estimates. The ranges of the individual alternative TSI estimate series vary between 0.49 and 4.64 Wm-2, with a median range of 1.77 Wm-2, while IPCC Solar has a range of only 0.19 Wm-2.
In this study, we consider two plausible TSI reconstructions from Connolly et al. (2023). Those from Hoyt and Schatten (1993) and from Bard et al. (2000), which Connolly et al. (2023) updated to the year 20182. The former TSI record (“H1993 Solar”) was based on the so-called multiproxy—i.e., equatorial solar rotation rate, sunspot structure, the decay rate of individual sunspots, the number of sunspots without umbrae, and the length and decay rate of the 11-yr sunspot activity cycle—reconstruction of the solar irradiance history.
1.2 Alternative hypotheses on temperature estimation
The IPCC’s attribution studies do not account for the direct effects of human activities on local temperatures (heat islands)—the second weakness addressed in this study. For example, heating and cooling of building interiors, electricity generation, manufacturing, freight and transport, asphalt and concrete, and where vegetation and open water have been removed or added. Where temperature readings are taken close to such human sources of heat or absence of natural cooling, they cannot properly reflect the individual effects of human emissions of carbon dioxide, etc., that the IPCC are concerned about (their Anthro variable), the Volcanic variable, and TSI.
To address this second limitation in the IPCC attribution studies, Connolly et al. (2021, 2023) developed four alternative estimates of surface temperatures that were intended to avoid heat island effects. They were based on rural only weather station readings, sea surface temperature readings, tree-ring width measurements, and glacier length measurements. For comparison with the approach used by the IPCC, they also developed an all-land temperature estimates series for the Northern Hemisphere.
1.5 Hypotheses tested
The foregoing discussion suggests the following hypotheses, which are tested in this study.
- H1. Forecasts from causal models will [will not] be usefully more accurate than forecasts from a naïve no-change model.
- H2. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] have greater predictive validity.
- H3. The statistical fit of the models (adjusted-R2) will not [will] be substantively positively related to their predictive validity.
- H4. Models using variable measures developed independently of the IPCC dangerous manmade global warming hypothesis will [will not] be more reliable.
Findings
3.1 Predictive validity of causal models versus naïve model [H1]
Forecast errors were larger than the benchmark errors (UMBRAE) for the IPCC Anthro models AVL and AVSL estimated with data from 1850 to 1949 and from 1850 to 1969, and for the AVR and AVSR models estimated with data from 1850 to 1899, 1850 to 1949, and 1850 to 1969. The anthropogenic warming models showed predictive validity relative the naïve model (UMBRAE less than 1.0) for only three of the eight combinations of forecast variable and estimation sample period.
3.2 Predictive validity of independent versus IPCC models [H2]
The MdAEs (median absolute error) of the forecasts from the models with IPCC’s anthropogenic and volcanic series as causal variables (AVL and AVR) and from the models that also included IPCC’s solar series (AVSL and AVSR) were greater than 1°C (roughly 2°F) for five of the eight combinations tested. The MdAEs of the forecasts from the models with B2000 solar and the volcanic series as causal variables (SBVL and SBVR) were less than 0.55°C (1°F) for all eight of the estimation periods used and temperature series being forecast combinations and for seven of the eight in the case of the models with H1993 as the solar variable (SHVL and SHVR).
3.3 Relationship between predictive validity and statistical fit of models [H3]
The correlations (sign-reversed Pearson’s r) between the accuracy of out-of-sample forecasts, as measured by UMBRAE (an error measure, hence the sign reversal), and the statistical fit of the models to the estimation data (adjusted-R2) for the causal models tested were large and negative for six (6) of the eight (8) combinations of estimation period (1850 to 1899, 1949, 1969, and 1999) used—and hence maximum forecast horizon of 119, 69, 49, and 19 years, respectively—and temperature series (NH Land and NH Rural) forecast.
3.4 Reliability of independent versus IPCC models [H4]
Charts of the results of Test 2 are presented in Figure 2 and are discussed below.
The independent solar models—SBVL and SHVL, and SBVR and SHVR—perform largely as one
would expect of causal models when forecasting using known values of the causal variables.
In the case of the AVR and AVSR models—forecasting the rural land temperatures, on the right of Figure 2—the MdAEs decreased rapidly from roughly 17 times the corresponding naïve forecast errors to beat the naïve MdAE when the 76th observation (1925) was added to the estimation samples. After that observation was added, the MdAEs for the AVR and AVSR model forecasts increased rapidly with each extra observation then stayed high before rapidly declining again after the 116th observation (1965) was added to the estimation samples.
When a model of causal relationships is estimated from empirical data on valid causal variables reliably measured, one would expect forecast errors to get smaller as more observations are used in the estimation of the model’s parameters. That is what the charts in Figure 2 show in the case of the naïve benchmark model forecasts and, broadly, what can be seen in the case of the independent models SBVL, SHVL, SBVR, and SHVR, but is not seen in the case of the models using the IPCC variables: AVL, AVSL, AVR, and AVSR.
The errors of the Anthro models’ forecast errors explode well beyond 1 °C and the benchmark model errors for forecast years beyond the mid-1970s, with puzzling exceptions. Namely, forecasts from Anthro models estimated from the largest sample size in the chart—1850 to 1999—and from models estimated from the smallest sample—1850 to 1899—forecasting All Land temperatures. In those cases, involving three of the eight charts, the Anthro model errors are less than the median historical temperature benchmark model errors, and mostly less than the errors of the independent models in later years.
The explosion in Anthro model errors from the 1970s is more extreme for models estimated to forecast Rural Land temperatures. Moreover, for the models estimated using only 1850 to 1899 data, errors are larger than those of the benchmark and independent models from 1920 and, prior to 1970, without any obvious pattern.
5. Conclusions
The IPCC’s models of anthropogenic climate change lack predictive validity. The IPCC models’ forecast errors were greater for most estimation samples —often many times greater—than those from a benchmark model that simply predicts that future years’ temperatures will be the same as the historical median. The size of the forecast errors and unreliability of the models’ forecasts in response to additional observations in the estimation sample implies that the anthropogenic models fail to realistically capture and represent the causes of Earth’s surface temperature changes. In practice, the IPCC models’ relative forecast errors would be still greater due to the uncertainty in forecasting the models’ causal variables, particularly Volcanic and IPCC Solar.
The independent solar models of climate change—which did not include a variable representing the IPCC postulated anthropogenic influence—do have predictive validity. The models reduced errors of forecasts for the years 2000 to 2018 relative to the benchmark errors for all, and all but one of 101 estimation samples tested for each of the two models. One of the models (B2000 Solar) reduced errors by more than 75 percent for forecasts from models estimated from 35 of the samples—a particularly impressive improvement given that the benchmark errors were no greater than 1 °C for all but one of the estimation samples.
The independent solar models provide realistic representations of the causal relationships with surface temperatures. The question of whether the independent solar variables can be forecast with sufficient accuracy to improve on the benchmark model forecasts in practice, however, remains relevant. All in all, and contra to the IPCC reports, there is insufficient evidential basis for the use of carbon dioxide, et cetera, emissions—taken together, the IPCC’s Anthro—as climate policy variables.
Read more:
https://t.co/cv3iFjOLTs](https://pbs.twimg.com/media/Grod_UiWMAAG0Y-.png)
Post McDonald's clarity is insane. Why did I do that
The world took a giant leap forward into totalitarianism yesterday as the Brazilian government blocked X, formerly Twitter, and threatened to fine its citizens $8,900 per day if they use it.
Brazil, the world's sixth largest nation by population, now joins North Korea, China, and Iran in the list of countries that have banned X. It is the 12th largest economy in the world and the superpower of the global South.
It doesn’t matter whether or not you care about Brazil. Its totalitarianism is at risk of spreading around the world.
Yet neither President Joe Biden, Vice President Kamala Harris, nor anyone else in the US government has formally denounced the censorship. That’s probably because Biden, Harris, and Democrats not only support the censorship but have also been directly funding it.
Of course, there is authentic demand for censorship from within Brazil, and it is ultimately up to the Brazilian people to reverse the nation’s descent into totalitarianism. Brazil’s powerful government-funded news media corporations have been urging censorship, which benefits President Lula’s ruling Workers’ Party. It’s up to Senate President Rodrigo Pacheco to impeach the Supreme Court Justice turned dictator, Alexandre de Moraes.
At the same time, the Biden administration and Democrats have played an influential role in instigating censorship in Brazil. The FBI went to Brazil and gave its Supreme Court justices advice on how to censor its population. The US government has been funding many of the NGOs in Brazil that have demanded censorship since the election of a populist president there in 2018. And when I testified before Congress in May of this year, the Democrats on the committee and their witness defended Brazil’s censorship.
And Pacheco yesterday affirmed his support for Moraes.
After the election of populist president Jair Bolsonaro in 2018, the US government funded pro-censorship organizations that demanded censorship. Moraes yesterday openly defended censorship to prevent “extremist populist groups” from coming to power. As United States Federal Communications Commissioner Brendan Carr noted yesterday, that’s a direct violation of the Brazilian constitution, which explicitly protects political speech.
As such, the totalitarian takeover of Brazil is a model for what the Democrats and the legacy news media want for the whole world. Pro-censorship scholars at Stanford and Harvard, Democrats in Congress, and the US news media have long recognized that the First Amendment is an obstacle to their plans. And so they have supported censorship efforts by nations with weaker free speech protections, like Brazil, Britain, Australia, and the European Union, to censor and even block X.
It’s important that we fight back. We need to show our support for free speech. I urge my Brazilian friends to stay on the platform. Everyone knows that it would be extremely difficult for Moraes to enforce his insane decree, much less fairly and equally. Proof of this comes from the fact that the Brazilian government and the ruling Workers’ Party are still using X, which means they are also using VPNs to evade their own ban.
Brazil is thus not just a dictatorship but a lawless one. Its leaders are behaving like the hypocritical pig leaders in George Orwell’s Animal Farm.
And independent Brazilian politicians including Senator Eduardo Girão, Congressman Marcel Van Hattam, and Vice Governor of Minas Gerais, Mateus Simões are all resisting the ban and posting on X. “I never imagined myself practicing and propagating civil disobedience,” said Simões, “but censorship cannot be tolerated, ever.”
If you’re not already a premium subscriber to X, please consider becoming one. If you can do more, please consider subscribing to Public. And if you can do even more than that, please make a tax-deductible donation to the free speech movement here:
https://t.co/iaiEtv1t3H
Biden and Harris and other political leaders must denounce what is happening. It’s incredible that the only US government official to criticize Brazil’s dictators is the FCC’s Brendan Carr. Congress must hold hearings. People around the world should recognize Brazil’s banning of X as an attack on free speech everywhere. Everyone is at greater risk of totalitarianism when major countries like Brazil succumb to it.
The good news is that the whole world is watching and many in the legacy news media in Brazil are denouncing the government’s ban on X. It’s not too late to turn things around. The world took a giant leap toward dystopia yesterday. But a worldwide free speech movement can pull Brazil back. It’s time to make 1984 and Animal Farm fiction again.
Half time analysis of Spain 0-0 Italy with the king of Spain.
Not a joke.
I've worked in a bank (10yrs), a hedge fund (6yrs) and in HFTs (4yrs). Two things:
1. Yes, I'm old
2. You be amazed - stunned - by the number of premium name counterparties whose polished branding and external image belies a myriad of internal 'systems' that are held together by excel + a bunch of batch processes written in a language that only a single contractor knows how to maintain
@myGridGB what are you using to calculate the carbon intensity?
@SalomeSibonex Spot on
"Visit all the places, eat all the food, read all the books, make all the friends, gather all the fame, support all the causes, build all the products, earn all the money... ...to see that the quality of your life is what it is when you are doing nothing."
@naval
it was totally random
I've always been puzzled by how many abandoned and buried cities there are. What happened to entire civilizations?
Now we are watching human population collapse in a number of countries.
Makes me wonder if all human civilizations fail for the same reasons. What would those reasons be?
My guess is that pockets of humans develop belief systems that drive success, but success creates space for diverse opinions, and then the winning mindset is destroyed.
That's what is happening in America. Traditional American values -- with all its flaws -- created a strong country. Now we have the luxury of entertaining less productive mindsets that are destroying the foundation.
Maybe that happens to every civilization.
AI visual translation from FlawlessAI:
The Spanish one looks so real!!
No wonder Sam Altman from OpenAI recently said:
"AI will break capitalism"
Because it probably will...
Watch the full video until the end to see why
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