My husband, John Abbot, has kept going with research into AI and climate forecasting. One of his most important recent papers can be downloaded from Earth Sciences.
Some of the latest Nobel prizes in science, including in chemistry, have been for developments in artificial intelligence (AI). Of course, John Abbot and I began working on the application of this technology for forecasting rainfall soon after Brisbane flooded back in 2011. Back then, we had just started dating. It would be some years before we got married.
We had a first AI model developed just a couple of months later, this included some hindcasting showing that the flooding of Brisbane could have been forecast given the temperature and pressure setup across the Pacific Ocean through 2010, and natural climate cycles.
John Abbot and I published a series of papers together through until 2017, after that I lost interest in developing the temperature series that were part of the arrays that John inputted into the neural networks.
I gave-up in 2017, in part because of the hatred that was directed at me, and John, including on Twitter with what was the publication of perhaps our most important paper, newly published in the journal GeoResJ on the application of artificial intelligence, for evaluating anthropogenic versus natural climate change (GeoResJ, Vol. 14, Pgs 36-46 published in July 2017). This research was pilloried on Twitter, and we were defamed by Graham Readfearn in The Guardian. Meanwhile, the editor of the journal was under intense pressure for the paper to be retracted. And some time was expended in writing what seemed like endless letters in its defence. The paper was never retracted, but the journal was closed.
I also gave-up, because I lost confident in the integrity of the actual temperature measurements being recorded by the Australian Bureau of Meteorology. Previously I put a lot of work into ensuring that we inputted unhomogenised data. Then, when I realised the extent to which the new platinum resistance probes could be calibrated to basically measure up or down relative to a mercury thermometer and in fact, they were being recalibrated to measure hotter – well I gave up, in tears. My focus then become getting the parallel data to better understand the relationship between the two different types of temperature measuring.
I put a lot of effort into this until last April 2023, that was after the Administrative Appeals Tribunal Hearing, a front-page story in The Australian, and an understanding that not even the Conservative side of politics had any interest in holding the Bureau to account on this fraudulent remodelling. I understood that we were on our own.
Timing is everything they say, if one is to be successful at forcing change, and so it will need to be a team effort, we will need to be supported, we will need to wait.
Meanwhile, John Abbot, who tends to be less affected by the politics, and the injustices, has continued to run the AI. Instead of relying on me for long continuous temperature series, he began using much longer, and already published temperature series. And from both those who believe in the existence of Medieval Warm Period and those who dispute its existence.
He had an important paper published in 2021, further showing the application of AI for distinguishing natural from anthropogenic climate change. This research concludes:
The presence of oscillatory characteristics within proxy- temperature reconstructions across a range of time-scales, including millennial, centennial and decadal, has been reported in many investigations. The present study examines oscillations from spectral analysis applied to eight published multi-proxy temperature records, including examples representing both hockey stick [catastrophic recent warming] and MWP_LIA* cycles [it was warmer in the past during the medieval warm period].
The analysis shows that each record can be represented by a set of 4-11 sine curves from spectral analysis that include a dominant millennial oscillation and several centennial and decadal oscillations. The apparent divergence into either a hockey stick or MWP_LIA* cycles can be derived from the phase alignment of the centennial and decadal oscillations with respect to the millennial oscillation. The maximum temperature of the dominant oscillation at around 1000 AD is increased by superimposing the centennial/decadal oscillations for MWP-LIA* cycles, whereas it is reduced and the profile flattened for the hockey stick. This may explain why current temperatures may exceed any in past 1,000 years with hockey stick profile.
Forecasting through projection of pre-industrial temperature oscillatory patterns beyond 1880 AD by applying spectral analysis to generate input to train ANNs show that current atmospheric temperatures can be largely explained on basis of continuation of natural oscillations. This is the case irrespective of whether the hockey stick or MWP_LIA* cycles are operative. This process could give rise to temperatures higher that past 1000 years without major contribution from anthropogenic influences.
*Medieval Warm Period – Little Ice Age.
It is surprising that these important findings are not of more interest to those who claim to care about science and especially climate science.
Back in 2011, in fact right up until 2017, John and I were laughed at, including by our colleagues for suggesting the relevance of AI for elucidating natural climate cycles and using these same cycles to forecast monthly rainfall.
But perhaps the time is coming when it will be realised that we were just a bit ahead of the times.
Of course all our work was, and continues, to be funded by the B.Macfie Family Foundation through the Institute of Public Affairs for which we are grateful.
This article is not behind a paywall, it can be downloaded for free at this link.
Relevant and related blog posts
https://climatelab.com.au/2023/05/averaging-last-seconds-versus-bureau-peer-review/
2. Holding Bureau to account:
https://climatelab.com.au/2016/02/rainfall-forecasts-should-be-benchmarked/
3. Confused and misguided colleagues:
https://ipa.org.au/ipa-today/cyclone-jasper-bom-forecasting-getting-to-the-truth/
Philip Mulholland MSc has emailed, requesting I add this comment to the thread.
There are many natural processes that respond to and record environmental influences, such as seasonal temperature cycles. These processes include (but are not limited to):
1. Formation of glacial ice layers and ice caps whose thickness is recorded by coring
2. Lacustrine Sedimentary layers (Varves) also recorded by coring.
3. Growth patterns of Marine Corals
4. Growth patterns of tree rings (Dendrochronology).
5. Cave formation stalactites and stalagmites (Speleothems) formed by calcite minerals deposited during dissolution from water.
All these processes leave a natural record that can be collected and studied by data analysis. Seasonal and annual variations in layer thickness form a basic qualitative data series. However, a more robust analysis involves determining the stable element isotopic ratio of carbon and oxygen atoms in the material being measured. It is by this means that the relationship between the soft qualitative environmental data of layer thickness and the hard numerical data of temperature can be established. For this link to occur there must be a plausible geophysical process that determines variations in isotopic ratio.
Consider the case of cave speleothems. One of the most noticeable features of a cave environment in the vadose zone (the air-filled voids above the water table) is the constant long-term air temperature. So how is the step from a constant temperature cave environment to obtain a climate signal made? The answer lies in the analysis not of crystal layer thickness but in the stable isotopic ratio of the crystal structure. The link back to climate variability comes from the measured and established fractionation of light 16O water versus heavy 18O water https://en.wikipedia.org/wiki/Oxygen_isotope_ratio_cycle
The fractionation of precipitation water (rainfall and snowfall) in favour of the light 16O isotope that was reported by the Danish paleoclimatologist Willi Dansgaard, (1964) is a real geophysical process that produces a temperature signal which can be measured, quantified, and mapped to identify a valid climatic temporal response, even in the stable environment of a cave.
The use of isotope fractionation is also used in ice core analysis, but is not currently used in dendrochronology. This is why the 1974 work of Leona Marshall Libby is so important. (Libby, L.M. and Pandolfi, L.J., 1974). It takes us away from the measurement of ring thickness on an individual tree by tree basis that is highly variable, to the common overarching control on tree growth of atmospheric physics (temperature and isotopic composition).
It is imperative to get away from the kludge of mashing thickness data (tree rings and ice lake varves) into a data stream based on isotopic ratios (ice core and tropical cave speleothems) that forms the basis of the iconic hockey stick of Michael Mann. However, the key problem with the hockey stick is not just this mismatch of mixed data types (a correlation issue) it is in fact the calibration issue of applying a varve data set that has literally been inverted to damp the historic temperature response.
The use of the Korttajärvi lake varve data from Finland Tiljander et al (2003) is discussed in detail by Steve McIntyre in 2008 here in Mann et al 2008: Korttajärvi https://climateaudit.org/2008/09/03/mann-et-al-2008-korttajarvi/
In this 2008 post Larry Huldén of the Finnish Museum of Natural History writes:
The Finnish Lake sediments cannot be used for temperature interpretations in the 18th to 20th century unless you know exactly the history of the regional lake environment conditions. We have 180,000 lakes in Finland. It is very easy to cherry pick among them and say that it is a random sample. Of all lakes, 1,500 lakes are affected by lowering of water levels. These must be omitted. Many other are affected by agriculture including forestry. This affects the relative components of the sediments. This is well known although somebody can by chance use them for climate trends. Finnish prof. Matti Saarnisto has showed me graphs of lake sediments from Finland which can be used for temperature trends but still show strong deviations in the recent 200 years because of agriculture. These lakes are not always very close to agricultural sites.
In addition, we must remember that the fauna or flora in the sediments do not represent the temperature of the air because long term trends in water temperatures do not correlate with long term trends in air temperatures.
Fast forward from 2008 to 2021 where we have John’s work Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures. Earth Sciences, 10(3), 95-117 in which he states:
One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation.
What I find interesting about John’s assessment is that he is implying that the iconic Hockey Stick reconstruction of Professor Mann and the foundational climatic reconstruction of Professor HH Lamb, are both representations of the same data resource that can be used to support conflicting climate narratives.
References:
Dansgaard, W., 1964. Stable isotopes in precipitation. Tellus, 16(4), pp.436-468. https://www.tandfonline.com/doi/pdf/10.3402/tellusa.v16i4.8993
Libby, L.M. and Pandolfi, L.J., 1974. Temperature dependence of isotope ratios in tree rings. Proceedings of the National Academy of Sciences, 71(6), pp.2482-2486. https://www.pnas.org/doi/pdf/10.1073/pnas.71.6.2482
Tiljander, M.I.A., Saarnisto, M., Ojala, A.E. and Saarinen, T., 2003. A 3000‐year palaeoenvironmental record from annually laminated sediment of Lake Korttajarvi, central Finland. Boreas, 32(4), pp.566-577. http://www.climateaudit.info/pdf/paleolimnology/Tiljanderetal.pdf
https://media.bom.gov.au/releases/18/bureau-of-meteorology-media-statement-no-2-climate-records/
Sounds more like a conspiracy theory promoted by climate change deniers. BoM backs their results up with actual rigorous data, not allegations. It's all out there if you care to look.
Some are very sensitive regard the homogenization rorting and continue to attempt to justify the deliberate adulteration of the actual data to achieve a preconceived target. A bit like the flat earth society of the day.