Ever since the early 2000s climate change has been big news. John Sutter of CNN (2015) recently quoted Gina McCarthy (Environmental Protection Agency, USA) stating that “climate change is the greatest threat of our time”. The effects of climate change are of huge interest here in New Zealand where we live directly under an expanding ozone hole. And of course in extreme climates such as India and Africa the impact of climate change is potentially devastating where communities live and work in already extreme temperatures.
Around the globe there is ongoing debate as to whether climate change is a real phenomenon or simply a short-sighted perspective on normal cycles in weather patterns. John Coleman (2015) is outspoken about the integrity of climate change studies, believing that “heat waves are actually diminished, not increased”; a view supported by Professor Geoffrey Duffy (2010) who argues that “climate is always changing, and always will”. Even scientists who have made careers investigating climate change are cautioning against the public and political paranoia surrounding the topic, with noted New Zealand scientist David Evans (2011) suggesting that some of the claims are “outrageous fiction”. The cautionary views of scientists such as Evans and Duffy argue that empirical, scientific evidence does not support global warming. The facts simply ignored by political parties who use climate change as their main political platform.
However, even if we agree that global warming is a fiction and regardless of the debate on climate change, we can not ignore the physiological impact of extreme temperatures. Living and working in extreme heat is a serious problem, particularly in the ‘base-of-the-pyramid’ workforce of India, Africa and South East Asia. Overworked communities, suffering under poverty and substandard conditions are a humanitarian problem. For corporations who operate in these environments there are questions of sustainability, social responsibility and economic return as well. So while the science if often funded on the back of climate change, there are very real social and economical interests in the accurate measurement of physiological heat stress.
The UK’s Health and Safety Executive (2010) report the wet bulb globe temperature (WBGT) index as the most widely adopted measure of the effects of temperature on humans. However the direct measurement of WBGT is highly inaccurate, largely due to the difficulty of accurately recording the natural wet bulb temperature (Tw). It is the approximation of Tw that I have been particularly interested in over the past couple of years. In this series of blog posts (which I will link to below as they are published) I will investigate the current models for approximating Tw; including the most widely accepted iterative approach (see Lemke and Kjellstrom, 2012), an empirical model developed via genetic programming (see Stull 2011) and I will investigate the use of nonlinear least squares regression for modelling the relaxation of Tw.
My aim is not to set the academic world afire with this research, but simply to explore the properties of the approximation of WBGT, which is fundamentally defined by the iterative relaxation of Tw. The iterative approach is generally regarded as the the most accurate and suitable method at present. However, like many iterative algorithms if fails to scale to large data sets. I am intrigued that given the relaxation process is well understood, there is no current mechanistic model for this. It could be that the relaxation is highly sensitive to the inputs and that there is no simple relationship between the inputs and the final WBGT. To complicate this further, there are issues with missing data, anomalies and potential inaccuracies with the method of interpolation used by the European Climatic Research Unit (CRU) (New, Holme and Jones (2010) and http://www.ipcc-data.org/observ/clim/cru_climatologies.html).
My hope is to be able to derive a useful model for the relaxation of Tw, but failing this hopefully in attempting I can shed some light on the inherent spatial qualities and inaccuracies within the data set that make the approximation of WBGT such a tough problem.
Part One: Exploring the Relaxation of Tw
Coleman, J. Climate Chang is a lie, global warming is not real [News Blog]. (June 9, 2015). Retrieved from http://www.express.co.uk/news/clarifications-corrections/526191/Climate-change-is-a-lie-global-warming-not-real-claims-weather-channel-founder
Duffy, G.G. Climate Change – the real cause [Webpage]. (August 3, 2010). Retrieved from http://www.climaterealists.org.nz/node/601
Evans, D. Climate Models Go Cold [Blog Article]. (April 8, 2011). Retrieved from http://www.climaterealists.org.nz/node/798
Health and Safety Executive. Wet bulb globe temperature index [Webpage]. (August 15, 2010). Retrieved from http://www.hse.gov.uk/temperature/heatstress/measuring/wetbulb.htm
Lemke, B., & Kjellstrom, T. (2012). Calculating workplace WBGT from meteorological data: a tool for climate change assessment. Industrial health, 50(4), 267-278.
New, M., Hulme, M., & Jones, P. (2000). Representing twentieth-century space-time climate variability. Part II: Development of 1901-96 monthly grids of terrestrial surface climate. Journal of Climate, 13(13), 2217-2238.
Stull, R. (2011). Wet-bulb temperature from relative humidity and air temperature. Journal of Applied Meteorology and climatology, 50(11), 2267-2269.
Sutter, J.D. EPA boss: Climate change could kill thousands [News Blog]. (July 22, 2015). Retrieved from http://edition.cnn.com/2015/06/22/opinions/sutter-epa-climate-cost/