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.
New studies reported by CNN (here) are simply off-target. Why? Because they are based on unreliable climate models that rely on representations, not physics. Consider the graph below where the speculations of over 100 model runs are compared to actual temperature measurements.
Kravtsov, S. (2017), Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century, Geophys. Res. Lett., 44, 5749–5757, doi:10.1002/2017GL074016.
Identification and dynamical attribution of multidecadal climate undulations to either variations in external forcings or to internal sources is one of the most important topics of modern climate science, especially in conjunction with the issue of human-induced global warming. Here we utilize ensembles of twentieth century climate simulations to isolate the forced signal and residual internal variability in a network of observed and modeled climate indices. The observed internal variability so estimated exhibits a pronounced multidecadal mode with a distinctive spatiotemporal signature, which is altogether absent in model simulations. This single mode explains a major fraction of model-data differences over the entire climate index network considered; it may reflect either biases in the models’ forced response or models’ lack of requisite internal dynamics, or a combination of both.
“One of the most popular alarmist arguments is likening the “consensus climate scientists” to medical doctors. For example, this essay on “climate denial” from Andrew Winston at medium.com took part in the bashing of recently hired climate skeptic Brett Stevens at the NYT.” click here for WUWT
According to overseers of the long-term instrumental temperature data, the Southern Hemisphere record is “mostly made up”. This is due to an extremely limited number of available measurements both historically and even presently from the south pole to the equatorial regions. click here for the notrickszone
US House of Representatives Committee on Science, Space and Technology
Climate Science: Assumptions, Policy Implications, and the Scientific Method; Wednesday, March 29, 2017, 2318 Rayburn House Office Building.
Written testimony can be found here.
“In late 2015, Soon, Connolly, and Connolly (hereafter SCC15) published a comprehensive (101 pages) analysis of how the modern anthropogenic global warming (AGW) paradigm has been constructed. The paper, published in Earth Science Reviews, is entitled Re-evaluating the role of solar variability on Northern Hemisphere temperature trends since the 19th century.” click here