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Celestial climate driver: a perspective from four billion years of the carbon cycle
- Geoscience Canada
, 2005
"... The standard explanation for vagaries of our climate, championed by the IPCC (Intergovernmental Panel on Climate Change), is that greenhouse gases, par-ticularly carbon dioxide, are its principal driver. Recently, an alternative model that the sun is the principal driver was revived by a host of emp ..."
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The standard explanation for vagaries of our climate, championed by the IPCC (Intergovernmental Panel on Climate Change), is that greenhouse gases, par-ticularly carbon dioxide, are its principal driver. Recently, an alternative model that the sun is the principal driver was revived by a host of empirical observa-tions. Neither atmospheric carbon diox-ide nor solar variability can alone explain the magnitude of the observed tempera-ture increase over the last century of about 0.6°C. Therefore, an amplifier is required. In the general climate models (GCM), the bulk of the calculated tem-
The myth of dangerous human-caused climate change. Australasian Institute of Mining and
- Metallurgy, New Leaders Conference, Brisbane, May 2-3 2007, Conference Proceedings
, 2007
"... Whether dangerous human-caused climate change is a fact, possibly a fact or a fabrication depends on who you choose to believe. Many of us line up somewhere between probable and possible on this spectrum. (John Roskam, Australian Financial Review, 2006.) I have been dismayed over the bogus science a ..."
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Whether dangerous human-caused climate change is a fact, possibly a fact or a fabrication depends on who you choose to believe. Many of us line up somewhere between probable and possible on this spectrum. (John Roskam, Australian Financial Review, 2006.) I have been dismayed over the bogus science and media hype associated with the (dangerous) human-induced global warming hypothesis. My innate sense of how the atmosphere-ocean functions does not allow me to accept these scenarios. Observations and theory do not support these ideas. (Professor Emeritus William Gray, Colorado State University, 2006.)
Power computations for intervention analysis
- Technometrics
, 2005
"... 1 In many intervention analysis applications time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful since it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming t ..."
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1 In many intervention analysis applications time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful since it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying ARIMA or fractional ARIMA model is known or can be estimated from the pre-intervention time series, the methodology for computing the required power function is developed for pulse, step and ramp interventions with ARIMA and fractional ARIMA errors. Convenient formulae for computing the power function for important special cases are given. Illustrative applications in traffic safety and environmental impact assessment are discussed. KEY WORDS: Autocorrelation and lack of statistical independence; ARIMA time series models; Environmental impact assessment; Forecast
Some examples of negative feedback in the Earth climate system
, 2004
"... Abstract: Temporal variability of daily time series for total solar irradiance at the top of the atmosphere, the Microwave Sounding Unit (MSU) based global, hemispherical and zonal average temperature for the lower troposphere and stratosphere together with 5 surface air temperature data, measured a ..."
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Abstract: Temporal variability of daily time series for total solar irradiance at the top of the atmosphere, the Microwave Sounding Unit (MSU) based global, hemispherical and zonal average temperature for the lower troposphere and stratosphere together with 5 surface air temperature data, measured at various meteorological stations have been studied by means of the structure function. From the growth rate of the structure function in the time interval between 32 and 4096 days it follows that the variability of the series represents an anti-persistent (AP) behavior. This property in turn shows a domination of negative feedback in the physical system generating the lower tropospheric temperature variability. Distribution of the increments over various ranges and correlations between them are calculated in order to determine the quantitative characteristics describing temporal variability.
Critical Topics in Global Warming: Supplementary Analysis of the Independent Summary for Policymakers
, 2009
"... The sun affects our climate in direct and indirect ways. The sun changes in its activity on timescales that vary from 11, 22, 80, and 180 years and more. A more active sun is brighter due to the dominance of faculae over cooler sunspots; in this way, the irradiance emitted by the sun and received by ..."
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The sun affects our climate in direct and indirect ways. The sun changes in its activity on timescales that vary from 11, 22, 80, and 180 years and more. A more active sun is brighter due to the dominance of faculae over cooler sunspots; in this way, the irradiance emitted by the sun and received by the Earth is higher during active solar periods than during quiet solar periods. The amount of change of the total solar irradiance (TSI) during the course of an 11-year cycle, based on satellite measurements since 1978, is about 0.1%. This was first discovered by Willson and Hudson (1991) from the results of the SMM/ACRIM1 experiment, and was later confirmed by Fröhlich and Lean (1998). This finding has caused many to conclude that the solar effect on climate is negligible; however, many questions still remain about the actual mechanisms involved and the sun’s variance on century and longer timescales. The irradiance reconstructions of Hoyt and Schatten (1997); Lean et
Nonparametric Estimation of Time-Varying Covariance Matrix in a Slowly Changing Vector Random Walk Model
"... time-varying covariance matrix in a slowly changing vector random walk model ..."
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time-varying covariance matrix in a slowly changing vector random walk model
Long-term variations of hydrological and climate time series from the German part of the Elbe River basin
- Hydrological Processes
, 2008
"... Abstract: Long-term variations in the structure of mean monthly hydro-meteorological time series from the German part of the Elbe River Basin are analyzed. Statistically significant correlations between the 2-15 yr. scale av-eraged wavelet spectra of the mean monthly climate variables and the North ..."
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Abstract: Long-term variations in the structure of mean monthly hydro-meteorological time series from the German part of the Elbe River Basin are analyzed. Statistically significant correlations between the 2-15 yr. scale av-eraged wavelet spectra of the mean monthly climate variables and the North Atlantic Oscillation (NAO)- and Arctic Oscillation (AO)- Index are found, which provides evidence that such long-term patterns in climate time series are externally forced. Application of Singular Spectrum Analysis (SSA) re-sults in major low-frequency modes for the basin precipitation of the Striegis, Ohre and the Elbe River that coincide with those detected in the discharge time series. The percentage of the variance explained by the annual cycle and low frequency components is clearly larger for the discharge than for precipi-tation. This manifests itself also through higher DFA (Detrended Fluctuation Analysis) Hurst parameter (H) estimates for discharge than for precipitation. Upon subtraction of the annual- and the major low frequency SSA- signal from the raw time series data, the DFA H parameter estimates suggest a short-range memory structure of the residuals for both precipitation and dis-charge. For the Este-River flows at gage Emmen we show additionally that the low frequency variability modes can be predicted by the SSA recurrent algorithm.
Describing temporal variability of the mean Estonian precipitation series in
"... climate time scale ..."
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"... ARIMA representation for daily solar irradiance and surface air temperature time series ..."
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ARIMA representation for daily solar irradiance and surface air temperature time series
ARTICLE Celestial Climate Driver: A Perspective from Four Billion Years of the Carbon Cycle
, 2005
"... The standard explanation for vagaries of our climate, championed by the IPCC (Intergovernmental Panel on Climate Change), is that greenhouse gases, particularly carbon dioxide, are its principal driver. Recently, an alternative model that the sun is the principal driver was revived by a host of empi ..."
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The standard explanation for vagaries of our climate, championed by the IPCC (Intergovernmental Panel on Climate Change), is that greenhouse gases, particularly carbon dioxide, are its principal driver. Recently, an alternative model that the sun is the principal driver was revived by a host of empirical observations. Neither atmospheric carbon dioxide nor solar variability can alone explain the magnitude of the observed temperature increase over the last century of about 0.6°C. Therefore, an amplifier is required. In the general climate models (GCM), the bulk of the calculated temperature
