I AM a scientist, natural historian and writer. I have more than a dozen technical papers published in peer-reviewed climate science journals and as book chapters. I have also published on mangroves, midges and many other things.
More recently I’ve been attempting to recast the climate narrative away from one of fear, towards awe in understanding natural climate cycles*.
I base my arguments and conclusions on evidence, and I apply logic. Of course, science is a method. Science is never ‘settled’. Those who appeal primarily to the authority of science and the notion of a consensus are more interested in politics. Central to the scientific method is the hypothesis that can be tested: that can potentially be falsified. We must therefore always be open-minded, tolerant and ready to be proven wrong.
I am also a senior fellow at the Melbourne-based Institute of Public Affairs. I am grateful for the IPA’s continuing financial support through the B. Macfie Family Foundation. A major project over the next year with the IPA will be the publication of the next book in the ‘Climate Change: The Facts’ series.
Towards the goal of improved weather and climate forecasting using Big Data and Artificial Neural Networks (ANNs), I incorporated the Climate Lab Pty Ltd. Over the last year I’ve delivered training program with the Queensland University of Technology (QUT) in big data and artificial intelligence for better climate services.
Since 2007, the contents of this website and blog have been archived each year by the National Library of Australia.
Jennifer Marohasy BSc PhD
Noosa, Queensland, Australia
17 March 2019
*Despite a significant inter-governmental investment in climate-related research over several decades, there has been no improvement in the performance of medium to long-range weather and climate forecasts. The current approach attempts to simulate actual physical processes, while assuming a dominant role for carbon dioxide as a driver of climate change. The future is in a radically different approach based on a new paradigm, already made possible by the advent of Big Data and Artificial Intelligence (AI) – specifically Artificial Neural Networks (ANNs). ANNs can be used to mine historical climate data for patterns, construct statistical models, and then using these to forecast.
This approach to forecasting does not involve the coding of equations that describe weather processes. Rather it involves mining historical data and building statistical models.
Measurements of any variable associated with weather and climate, when arranged chronologically at the appropriate scale, show patterns of recurring oscillations. The oscillations may not be symmetrical, but they will tend to channel between an upper and lower boundary – over and over again.
Most of these oscillations can be deconstructed into sine waves of varying phase, amplitude and periodicity. These oscillations may, or may not, represent real world phenomena that can be explained in terms of atmospheric physics and chemistry, and/or the gravitational-pull of the Moon, and/or variations in the electromagnetic field of the Sun, and its changing declination relative to the tilt of the Earth, et cetera – but they exist.
As long as the relationships embedded in the complex oscillation continue into the future – and it is these relationships that we model – an accurate forecast is theoretically mathematically possible.
Of course, the quality of data used to construct the arrays and entered into the ANNs is important. Thus, my longstanding concerns about the appropriate calibration of equipment used to measure surface temperatures by the Australian Bureau of Meteorology – and also the inappropriate remodelling of data through a technique known as “homogenisation”. I’ve detailed some of these concerns in a letter to the Chief Scientist.