Why belief in CAGW (Catastrophic Anthropogenic Global Warming) is not currently justified by the standards of the scientific method
Posted by Strategesis on May 9, 2012
Science does not deal in absolute proofs. The scientific method depends upon falsification of alternative hypotheses until only one remains.
But “falsification” in science is not absolute. Instead, it is a matter of relative probabilities. Such “proof” by falsification of all alternatives is never final: All scientific laws, theories and hypotheses forever remain subject to falsification at any time–at least in principle, even if the odds of that ever happening are infinitesimally small.
All that is required to falsify an hypothesis, or to falsify the currently-accepted theory, is for an alternative hypothesis to be shown–by empirical evidence and quantitative analysis of the relative probabilities–to have a statistically-significant higher probability of being correct.
The CAGW hypothesis is that a) The Earth’s climate is warming, b) The warming is substantially a result of human emissions of CO2 and, c) The magnitude of the warming will be enough to have significant effects, and d) The net effects of the warming will be harmful, and e) The harm caused by the warming will be great enough to be worth the net costs of politically-coerced mitigation.
The alternative hypothesis–which is also the null hypothesis (<= click the link for more info)–is that a) The warming is substantially due to natural causes for which humans are not substantially responsible, and/or b) The magnitude of any human-caused warming will not be not be great enough to have significant effects by itself (regardless of the effects of any warming not caused by man,) and/or c) The net effects of warming will not be harmfull–or if they are, then not by enough to be worth the cost of politically-coerced mitigation.
The null hypothesis has never been falsified. There have been no peer-reviewed studies published that quantitatively analyze both p(CAGW | Historical-Temperature-Data) [the probability that CAGW hypothesis is true, given the historical temperature data] and p(NullHypothesis | Historical-Temperature-Data) [the probability that the Null Hypothesis is true, given the historical temperature data], showing that the former (CAGW) has a statistically significant higher probability of being true than the latter (the null hypothesis–that warming is substantially natural.) Not one.
But the reverse is not true:
HOW NATURAL IS THE RECENT CENTENNIAL WARMING? AN ANALYSIS OF 2249 SURFACE TEMPERATURE RECORDS (Clickable link)
We evaluate to what extent the temperature rise in the past 100 years was a trend or a natural fluctuation and analyze 2249 worldwide monthly temperature records from GISS (NASA) with the 100-year period covering 1906–2005 and the two 50-year periods from 1906 to 1955 and 1956 to 2005. No global records are applied. The data document a strong urban heat island effect (UHI) and a warming with increasing station elevation. For the period 1906–2005, we evaluate a global warming of 0.58°C as the mean for all records. This decreases to 0.41°C if restricted to stations with a population of less than 1000 and below 800 meter above sea level. About a quarter of all the records for the 100-year period show a fall in temperatures. Our hypothesis for the analysis is, as generally in the papers concerned with long-term persistence of temperature records, that the observed temperature records are a combination of long-term correlated records with an additional trend, which is caused for instance by anthropogenic CO2, the UHI or other forcings. We apply the detrended fluctuation analysis (DFA) and evaluate Hurst exponents between 0.6 and 0.65 for the majority of stations, which is in excellent agreement with the literature and use a method only recently published, which is based on DFA, synthetic records and Monte Carlo simulation. As a result, the probabilities that the observed temperature series are natural have values roughly between 40% and 90%, depending on the stations characteristics and the periods considered. “Natural” means that we do not have within a defined confidence interval a definitely positive anthropogenic contribution and, therefore, only a marginal anthropogenic contribution cannot be excluded.
In other words, this study finds that the probability that the observed climate change is some combination of natural variability and urban heat island effect to be as high as 90%. That’s a very strong case in favor of the null hypothesis, and makes it extremely unlikely that there could be a 3-sigma difference in favor of the AGW hypothesis.
At best, the empirical evidence for human impact on climate change, more specifically, the anthropogenic global warming (AGW), is based on correlational research. That is, no experiment has been carried out that confirms or falsifies the causal hypothesis put forward by the International Panel on Climate Change (IPCC) that anthropogenic increasing of green house gas concentrations very likely causes increasing of the (mean) global temperature. In this article, we point out the major weaknesses of correlational research in assessing causal hypotheses. We further point out that the AGW hypothesis is in need of potential falsifiers in the Popperian (neopositivistic) sense. Some directions for future research on the formulation of such falsifiers in causal research are discussed. Of course, failure to find falsifying evidence in empirical climate data will render the AWG hypothesis much stronger.
Econometrics and the Science of Climate Change (Clickable Link)
Econometrics has a long history as the technique of choice for testing the merits of alternative hypotheses across most of the social sciences as well as many of the natural and materials sciences, not to mention pharmaceutical science, where it is widely used to evaluate the efficacy of alternative medications, including the use of placebos as counterfactuals. However its greatest value is in the social sciences where laboratory experiments are not feasible, but least squares linear regression can be used to assess the relative significance of alternative independent variables as explanatory factors. The founding texts of climate science, John Tyndall (1861) and Svante Arrhenius (1896), discovered and estimated the radiation absorption effects of what they called aqueous vapour and carbonic acid (now known as water vapour and carbon dioxide), unlike the oxygen and nitrogen that comprise the bulk of our atmosphere. Tyndall‘s experiments showed that the most powerful radiative effect was that of water vapour. Arrhenius also included water vapour in his more theoretical analysis.
Now however most climate scientists‘ models assume that anthropogenic addition to the atmospheric concentration of carbon dioxide (hereafter denoted [CO2]) and of certain other greenhouse gases like ozone and methane (in aggregate denoted as [CO2e]) is the major determinant of climate change, and have relegated Tyndall‘s primary role for atmospheric water vapour (hereafter [H2O]) to having only a secondary, or ―feedback, effect arising from the higher temperatures supposed to result primarily from increasing [CO2]. This assumption has never been validated by observations of the relative proportions of [H2O] that stem from solar radiation and rising surface temperature. Moreover the literature of climate science affords no evidence of the use of econometrics to test the core hypothesis that―most of the temperature change observed over the last century is attributable to the build-up in the atmosphere of anthropogenic emissions of CO2e, of which CO2 is by far the largest in volume terms, rather than being due to Tyndall‘s aqueous vapour [H2O]. In particular none of the leading texts such as the IPCC‘s Solomon et al. (2007), Stern (2006) and Garnaut (2008, 2011) performs or reports any econometric analysis of the core hypothesis.
This paper seeks to begin filling that gap, and finds that hypothesis is falsified at a wide variety of locations, oceans, and land masses (including Australia) with lengthy time series data on various climatic variables, including atmospheric water vapour [H2O]),and where available, opacity of the sky (OPQ), and solar radiation received at the earth‘s surface (SSR). Unlike Total Solar Irradiation – TSI – which is relatively constant, SSR is dependent inter alia on the amount of cloud cover. Multi-variate econometric analysis shows that at none ofthese locations, oceans, and landmasses is the role of [CO2] statistically significant, and even that it can be negatively correlated with changes in temperature, whereas [H2O] invariably plays a highly significant role. If the core hypothesis of climate science cannot be confirmed at any specific location, ocean, or landmass, then it cannot be confirmed for the globe even if a Popperian black swan could be found somewhere. In short, the econometric analysis of this paper fails to falsify the nul hypothesis of climate science, that there is no relationship between anthropogenic emissions of the main greenhouse gas, CO2, and observed temperature change.