On Government Climate Misinformation and Disinformation
The Australian parliament has setup a committee to investigate misinformation and disinformation. It is everywhere — misinformation and disinformation — and it is creating havoc in our communities and eroding public trust in many of our key institutions. The issue needs to be addressed.
It is not unique to one side of politics, this misinformation and disinformation. Indeed, it was John Howard’s conservative government that first put the legislation in place for Australia that now provides significant subsidies for a mandated transition to so-called renewables. This was prefaced on the nonsense idea that climate changing is something new; that the climate is on a new trajectory. Of course, there has always been climate change.
It is just over 20 years since John Howard introduced the renewable energy policy which required wind/solar-generated electricity, and the extent of the subsidies has grown and grown.
Twenty years ago, the official climate forecast for Australia was endless drought — we were told endlessly that the reservoirs would never fill again!
Soon they were overflowing, flooding my city of Brisbane, the capital of Queensland.
In fact, Australia has always been a land of drought or flooding rains. They have come and gone, in cycles.
The new parliamentary committee about misinformation and disinformation has established terms of reference, and I’ve provided two relevant case studies in my submission to this committee considering their terms of reference.
Click across to my Substack to read my full submission that I have already lodged with the government. My submission also includes information about my qualification and motivations, as requested by this parliamentary committee from anyone making a submission. https://jennifermarohasy.substack.com
And here is my first case study, that is part of my submission:
CASE STUDY #1.
USING BIG DATA AND AI TECHNIQUES FOR SKILFUL CLIMATE FORECASTING
After the catastrophic flooding of Brisbane in 2011, I pioneered a new technique for monthly rainfall forecasting using artificial neural networks, a form of AI. This work involves a knowledge of key variables affecting weather and climate developed from years of observation and reading, and the application of big data techniques.
This innovative research in rainfall forecasting resulted in a dozen publications in peer-reviewed international climate science journals and conference proceedings from 2012 to 2017. It also resulted in a collaboration with the Indonesian Bureau of Meteorology and publications through the Chinese Academy of Science. I am now extending this work to develop a new theory of climate resilience.
Rather than support this effort, the Australian Bureau of Meteorology, and others have accused me of “junk science” and climate denial. For example, in an article published by Crikey.com.au, it was written, and I quote:
“Jennifer Marohasy, who edited the IPA’s most recent book on climate is among the most prominent voices [concerning climate denial in Australia]. Last year, an article by Marohasy and fellow IPA member John Abbot managed featured in a peer-reviewed journal, gaining them considerable traction from right wing outlets like Breitbart, The Spectator and The Drudge Report. The article was panned by experts, with one referring to it as “junk science”. [End of quote] Link is here https://www.crikey.com.au/…/institute-of-public…/
Following is a summary of my research paper that has been repeated misrepresented as junk science and climate denial by high profile Australian and international climate scientists who find much political utility in the current paradigm and so they attack alternative models, and techniques such as my work using AI.
The specific paper referenced by Crickey.com as junk science is entitled, ‘The application of machine learning for evaluating anthropogenic versus natural climate change’ and was published by Elsevier in GeoResJ, Volume 14, December 2017, Pages 36-46, and can be access via https://www.sciencedirect.com/…/abs/pii/S2214242817300426 .
This research paper discusses the application of machine learning, specifically artificial neural networks, to evaluate the contributions of anthropogenic versus natural factors in climate change using temperature proxy data.
My study explores the use of machine learning, specifically artificial neural networks (ANNs), to evaluate the contributions of natural and anthropogenic factors to climate change.
- Time-series profiles from temperature proxies, such as tree rings, were analysed.
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ANNs were trained using sine wave components derived from these proxies to simulate historical temperatures.
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The largest deviation between ANN projections and actual temperatures was approximately 0.2 °C.
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An estimated Equilibrium Climate Sensitivity (ECS) of about 0.6 °C was derived, significantly lower than IPCC estimates.
My research highlights the importance of proxy records in understanding long-term climate variability.
- Proxy records include tree rings, corals, and sediments, providing evidence of past climate conditions.
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The late Holocene period showed oscillations in temperature, with significant events like the Little Ice Age and Medieval Warm Period.
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Natural phenomena, both internal and external, contributed to these oscillations prior to industrialisation.
My methodology involved spectral analysis and machine learning techniques to analyse temperature proxies.
- Six proxy records were selected from both hemispheres for analysis.
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Spectral analysis identified between 7 and 10 sine waves for each proxy record.
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The ANN models were optimised using Neurosolutions Infinity software, which automated the selection of the best model configurations.
My results indicate a close correspondence between ANN projections and actual temperature measurements.
- The ANN models were able to replicate historical temperature profiles effectively.
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Deviations between proxy records and ANN projections from 1880 to 2000 were minimal, suggesting natural oscillations largely explain temperature increases.
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The study found that the increase in temperature over the last century could be attributed mainly to natural phenomena.
My study contrasts its ECS estimates with those from the IPCC and other methods.
- The ECS estimated from this study is approximately 0.6 °C, significantly lower than the IPCC’s mean estimate of 3.2 °C.
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Spectroscopic methods also yield lower ECS estimates, with values ranging from 0.33 °C to 0.9 °C.
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The findings suggest a need for reconciling differences between GCM outputs and spectroscopic results regarding climate sensitivity.
This research work was funded by the B. Macfie Family Foundation and undertaken in part with Central Queensland University and James Cook University as part of a collaboration with John Abbot. Despite offers of collaboration with the Australian Bureau of Meteorology, there has been nothing but public derision, while scientists in CSIRO and at the Bureau of Meteorology have never published any form of rebuttal in the technical literature; they mostly have attacked this work on Twitter and in other places such as at Crickey.com.
Meanwhile they continue with outdated rainfall forecasting models and techniques including General Circulation Models that are far less skilful at weather and climate forecasting than the techniques I have developed using Big Data and AI.
The General Circulation Models forecast drought, and the experts said the Wivenhoe dam would never fill again. Then it rained and rained and Brisbane flooded again from the Wivenhoe dam that overflowed, and this photograph is from the aftermath of that flooding in January 2011.
I returned to Brisbane in January 2011 to help with the cleanup.
source https://www.facebook.com/JenniferMarohasyOfficialPage
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