John Abbot. Jennifer Marohasy. The application of machine learning for evaluating anthropogenic versus natural climate change. GeoResJ, Volume 14, December 2017, Pages 36-46.
Time-series profiles derived from temperature proxies such as tree rings can provide information about past climate. Signal analysis was undertaken of six such datasets, and the resulting component sine waves used as input to an artificial neural network (ANN), a form of machine learning. By optimizing spectral features of the component sine waves, such as periodicity, amplitude and phase, the original temperature profiles were approximately simulated for the late Holocene period to 1830 CE. The ANN models were then used to generate projections of temperatures through the 20th century. The largest deviation between the ANN projections and measured temperatures for six geographically distinct regions was approximately 0.2 °C, and from this an Equilibrium Climate Sensitivity (ECS) of approximately 0.6 °C was estimated. This is considerably less than estimates from the General Circulation Models (GCMs) used by the Intergovernmental Panel on Climate Change (IPCC), and similar to estimates from spectroscopic methods.
“Most people, to a greater or lesser extent, accept that carbon emissions are a problem which must be addressed. But with Al Gore there is no room for any uncertainties — you swallow whole the apocalyptic vision in his films or you are a ‘denier’. He and his ‘climate ambassadors’ whom he has trained to spread his message resemble a charismatic church whose leader must be paid constant homage. He is an obstacle to serious debate.” click here
Quansheng Ge, Haolong Liu, Xiang Ma, Jingyun Zheng, Zhixin Hao. Characteristics of temperature change in China over the last 2000 years and spatial patterns of dryness/wetness during cold and warm periods. Advances in Atmospheric Sciences. August 2017, Volume 34, Issue 8, pp 941–951
This paper presents new high-resolution proxies and paleoclimatic reconstructions for studying climate changes in China for the past 2000 years. Multi-proxy synthesized reconstructions show that temperature variation in China has exhibited significant 50–70-yr, 100–120-yr, and 200–250-yr cycles. Results also show that the amplitudes of decadal and centennial temperature variation were 1.3°C and 0.7°C, respectively, with the latter significantly correlated with long-term changes in solar radiation, especially cold periods, which correspond approximately to sunspot minima. The most rapid warming in China occurred over AD 1870–2000, at a rate of 0.56° ± 0.42°C (100 yr)−1; however, temperatures recorded in the 20th century may not be unprecedented for the last 2000 years, as data show records for the periods AD 981–1100 and AD 1201–70 are comparable to the present. The ensemble means of dryness/wetness spatial patterns in eastern China across all centennial warm periods illustrate a tripole pattern: dry south of 25°N, wet from 25°–30°N, and dry to the north of 30°N. However, for all centennial cold periods, this spatial pattern also exhibits a meridional distribution. The increase in precipitation over the monsoonal regions of China associated with the 20th century warming can primarily be attributed to a mega El Ni˜no–Southern Oscillation and the Atlantic Multidecadal Oscillation. In addition, a significant association between increasing numbers of locusts and dry/cold conditions is found in eastern China. Plague intensity also generally increases in concert with wetness in northern China, while more precipitation is likely to have a negative effect in southern China.
“This is #fakenews with icing and cherries on top, rings on its fingers, bells on its toes, a specially commissioned foreword by Al Gore and a rave review (“I love these lies. I could not have written better ones myself”) written from hell by the tormented shade of Josef Goebbels. No, actually, it might even be worse than that.” click here
The argument that “it is getting hotter around the world” simply cannot be supported by science. click here
Consider this comparison of temperatures based on extrapolations (top) with the available data fabrications (bottom). Record heat is claimed in Africa where no measurements were taken.
This study (here) of the affect of climate sensitivity on human pathogens is a literature search. The basic thesis being presented is that global warming will result in greater numbers of pathogens which in turn will result in more human illness. This line of thinking makes several assumptions. It might be useful in generating hypotheses for future surveillance but is inadequate for predicting future illness. Why? Because literature reviews are limited by what is called “publication bias”. That is, only certain articles and studies are publishable and others important studies relevant to this review are not published. Studies with negative findings are rarely published. Also, some studies are screened out because of reviewer bias. We can learn from this review but its interpretation is limited.
Indeed, survival of some pathogens may be expected to decrease. The authors themselves acknowledge:
“Although this study identifies a high degree of climate sensitivity among important pathogens, their response to climate change will be dependent on the nature of their association with climate drivers and impacts of other drivers.”
“One activist has drawn criticism from the scientific community by claiming it will render the planet uninhabitable and bring about “rolling death smogs” of pollution.” click here