Daily Archives: August 24, 2017

A ‘Locational Reference Frame’ for Assessing Climate Change Impacts on Infrastructure

F.W. Pontius. Sustainable Infrastructure: Climate Changes and Carbon Dioxide. American Journal of Civil Engineering Volume 5, Issue 5, September 2017, Pages: 254-267

Civil infrastructure provides the physical backbone of all societies. Water supply, wastewater treatment, transportation systems, and civil structures must be sustainable over multiple decades (e.g. 20, 30, 50 years) for human populations to survive and flourish. Over such a long time-period, climate changes are inevitable. The global atmospheric system is dynamic. Weather and climates are constantly adjusting. To date the effects of carbon dioxide have been evaluated almost exclusively using a global reference frame. However, civil infrastructure is stationary and local in nature. A locational reference frame is introduced here as an alternative framework for evaluating the effect of carbon dioxide on civil infrastructure. Temperature data from the City of Riverside, California from 1901 to 2017 are analyzed to illustrate application of a local reference frame. No evidence of significant climate change beyond natural variability was observed in this temperature record. Using a Climate Sensitivity best estimate of 2°C, the increase in temperature resulting from a doubling of atmospheric CO2 is estimated at approximately 0.009°C/yr which is insignificant compared to natural variability.

Fact Checking Mr. Al Gore’s ‘An Inconvenient Sequel’

“Meteorologist Roy Spencer has written a book which fact-checks Al Gore’s latest climate-disaster-porn movie An Inconvenient Sequel.” click here

Natural Factors Dominate Equilibrium Climate Sensitivity Estimated at 0.6C

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.