A lie will travel halfway around the world while the truth is lacing her boots, so the adage goes. And so, the showmen will continue to misrepresent natural disasters that befalls northern Australia while finding something popular to say at the very moment everyone’s attention is focused on that event.
For sure, the rainfall associated with Cyclone Jasper was extraordinary, but not unprecedented for the Cairns catchment. For sure, it is difficult to forecast weather and climate, but the skill of new systems based on artificial intelligence (AI) show great improvement, while the Australian Bureau of Meteorology remains wedded to its General Circulation Models.
Contrary to various popular claims, including by my colleague Peter Ridd*, the Bureau uses the same supercomputer and the same general circulation model to forecast rainfall whether considering the next three-hours, the next three months or the next three decades. It uses a simulation developed by the UK Met Office known as ACCESS-S2, that is also one of the Intergovernmental Panel on Climate Change’s CMIP6 models.
All the general circulation models are underpinned by the assumption that carbon dioxide drives climate change, and all these same models are focused on large scale processes making it difficult to accurately forecasts what matters to real people – local climate, especially extreme rainfall known to be associated with cyclones, and also seasonal rainfall deficient associated with drought.
The Bureau claims it can accurately forecast temperature to within 2 degrees Celsius on any day. This may be of intense political interest, but it is of little real value to the Australian community. Being able to accurately forecast rainfall would be much more meaningful.
There are four types of precipitation-forming processes, including cyclonic rotation (low pressure), though the IPCC’s models are all based on surface-heating (convection). It is perhaps for this reason that these models while accurately simulating general global patterns of rainfall remain unable to capture high intensity events over small areas including rainfall associated with cyclones. Some of these problems can be overcome through downscaling, but even then, it is unclear why the elevation of mountains is mostly underestimated while their spatial extent is overestimated. Fundamentally, and at the core of the problem, is that clouds formation occurs at a scale much smaller than the resolvable grid scale used within all the general circulation models.
The Bureau was able to accurately forecast the trajectory of Cyclone Jasper as it presented as a large and slow-moving weather system, but once the structure of the cyclone began to break down, and this occurred from December 9, ACCESS-S2 struggled to accurately forecast both direction and intensity. Worst, when the heaviest rainfall began eight days later, on December 17 in Cairns, the Bureau was unable to capture the extent of the downpour because its automatic weather recording system at Cairns airport failed. The same problem was experience during the Lismore flooding of April 2022, meaning that the true intensity of the rainfall from these events is not even properly documented.
I first became interested in rainfall forecasting using artificial intelligence in January 2011, following the flooding of Brisbane. My colleague John Abbot used artificial neural networks, a form of AI, for share market trading, and so successfully he bought a red corvette with the winnings one day. That same sports car was drowned in the 2011 Brisbane flooding.
Over the next five years to 2017, John Abbot and I successfully published a dozen research papers on our new technique using AI for monthly rainfall forecasting. We published in the best international peer-reviewed journals and as book chapters following AI conferences.
Our very first paper about forecasting monthly rainfall – for 17 locations in Queensland 12 months in advance – was published in Advances in Atmospheric Sciences which is an Elsevier journal sponsored by the Chinese Academy of Sciences.
Back 12 years ago, when we were pioneering the technique, the Chinese were very interested, and prepared to publish us, but not our own Bureau of Meteorology who scoffed at the idea that AI could be used for weather or climate forecasting.
“But the climate is on a new and dangerous trajectory”, said Oscar Alves who then headed-up the long-range forecasting unit at the Bureau in Melbourne.
At the time the Bureau was using a statistical technique for its medium-term forecasts, while busy developing its own general circulation model known as POAMA. POAMA was subsequently used for operational forecasts from June 2013, before being replaced by ACCESS-S1 in August 2018. POAMA proved a disaster and was replaced without even a media release announcing the change over.
It took the Bureau 20 years to develop POAMA, and they pulled it after just five years and so many disastrous rainfall forecasts that were never acknowledged.
John Abbot and I spent an afternoon with Alves, back in August 2011. John Abbot and I wanted to collaborate. We were convinced back then that AI could significantly improve the skill of the Bureau’s rainfall forecasts. Alves had no interest in learning anything new. Alves now heads up the Bureau’s Earth System Modelling unit.
All these years later and the Bureau is still refusing to consider the value of AI for forecasting rainfall extremes whether the consequence of a cyclone or a drought.
Meanwhile Google is now using AI for weather forecasting with their GraphCast, run on a desktop, outperforming all the GCMs run on supercomputers.
Google’s GraphCast works from the same principles John Abbot and I used: recurrent cycles that can be found in weather and climate data – as long as the data hasn’t been remodelled to fit the human-caused global warming theory. And the Chinese are now working on AI systems that can forecast both the intensity of rainfall during cyclonic events, as well as trajectory.
I have no doubt that the rainfall forecasts for North Queensland following landfall by Cyclone Jasper would have been far superior if ten years ago the Bureau had began to invest in AI technology, had began to develop some capacity in this very different technique.
There has been commentary suggesting Cyclone Jasper resulted in unprecedented rainfall in the headwaters of the Barron River causing flooding of Cairns, particularly of the Northern Beaches. There is a long continuous rainfall record for Kuranda indicating that while December 2023, and the rainfall associated with Jasper was extraordinary, there are higher totals for previous year going back to 1911.
The Australian emergency management minister, Murray Watt, has ordered a review of the weather warning systems used by the Bureau of Meteorology, while claiming it will become increasingly difficult to predict the weather because of climate change. He should be calling for much more than this, and not using the excuse of ‘climate change’ for both the failed 3-day rainfall forecast and also the failed warning system after the heavy rains began to fall.
For the last twenty years various international working groups associated with the IPCC and World Meteorological Organisation have been making submissions and predictions regarding the likely effects of global warming on tropical cyclones. These reports have indicated the maximum intensity of cyclones is unlikely to significantly increase, certainly not beyond 10-20 percent. A ‘Statement on Tropical cyclones and Climate Change’ in 2006 by Dr G. B. Love the Permanent Representative for Australia indicated that rainfall intensity could increase, due to the increasing water vapour content of the atmosphere. Twenty years later, the data shows that both the intensity and number of cyclones has been declining.
The extraordinary rainfall associated with the flooding of Lismore and surrounds in early 2022, may have been exacerbated by the increase in water vapour content from the explosion of the volcano Hunga Tunga in January of that year. It could be that the Hunga Tonga eruption has also caused a depletion of ozone in the stratosphere, after temporarily increasing the water vapour content. There has been no overall increase in water vapour content of the lower troposphere associated with increasing atmospheric levels of carbon dioxide.
The general circulation models have difficulty simulating the local impact of volcanic ash on rainfall intensity and global temperatures, and this is a problem because aerosols can supercharge the atmosphere making rainfall more intense.
Importantly, and contrary to recent popular commentary, there are no two separate parts to the Bureau: one making operational weather forecasts and one concerning itself with climate change. Since June 2013, forecasts for the next three-hours and next three-months have relied on a general circulation model and since 2018 specifically on ACCESS, with some adds-on to provide more resolution. There is an urgent need for the skill of this general circulation model to be properly assessed. This could be done as a matter of urgency and through a comparison of forecast versus predicted rainfall for the Cairns catchment as it fell through December 2023, particularly after cyclone Jasper made landfall.
There is also a need for the Bureau to quantify the skill more generally of ACCESS against the skill of the new AI weather and climate forecasting systems including Google’s GraphCast and the Pangu-Weather AI model. Pangu AI can predict both the direction of cyclones and their likely impact, particularly their capacity to generate intense rainfall over a small area.
There has been much talk about the unprecedented.
What would be both unprecedented and welcome would be for the Bureau to start doing KPI’s and measure predictions against actual rainfall totals. This needs to be done for the three-hour ‘rain burst’ events associated with low pressure systems and also their longer seasonal rainfall forecasts.
The Bureau forecast that this summer would be an exceptionally dry one for the same regions that have now flooded, using the same ACCESS general circulation model. The drought forecast has also caused unnecessary hardship, with farmers selling livestock at discounted prices as so many anticipated being unable to feed their stock.
Ends.
And just filing this here, by Peter Ridd and republished from The Australian, click here.
* Recent criticism of the Bureau of Meteorology for failing to predict the recent spate of extreme weather is unfair, is ultimately counter-productive and misses far more serious failings of the BOM.
Weather prediction is difficult. At best one can hope only to improve probabilities. And the weather hardest to predict is extreme events associated with storms. These systems are extremely “nonlinear”, to use the parlance of meteorology.
When there are large quantities of moisture in the lower levels of the atmosphere, the air need be lifted only slightly to trigger a violent updraft.
It is a huge slow-motion explosion where the fuel is the invisible water vapour turning into cloud. The amounts of energy involved can be huge – think Hiroshima atom bomb – and a tiny perturbation can set them off. It is often stated that a butterfly flapping its wings could trigger the storm, at least theoretically.
This is one of the least predictable phenomena on Earth. At best, weather prediction can indicate only that such storms are likely at a rough time and place. Perhaps the BOM can get the final warnings out a little faster, but a storm can morph into a supercell in a few minutes.
BOM’s performance in predicting the ultimate landfall of Tropical Cyclone Jasper was nothing short of brilliant. For days before it crossed the coast, the bureau predicted it would end up near Cairns. And that is where it went. The cyclone did minimal damage, but the rain cell associated with it sat stationary around Cairns for days, causing flooding. If the cell had moved, even slowly, Cairns would have been just extremely wet rather than breaking records. But that detail is beyond prediction.
The result of unjustified expectations of prediction accuracy will result in the bureau being forced to cover itself and issue warnings whenever there is a minute possibility of extreme weather. The predictions will become meaningless.
The BOM has a truly superb observation network of rain radars, rain gauges and flood levels. Millions of people use these, especially in country areas, for everything from bringing in the washing to gauging when it will be possible to drive across a flooded creek. This network gives us remarkable ability to see what is happening. Thirty years ago, we were almost blind compared with today.
So give the BOM a break, at least on this matter. But there are two BOMs. There is the operational weather BOM, which does the daily forecasts and measurements, and then there is the climate change part of the BOM. And that is where the criticism should be levelled.
The climate models used by the BOM and many other groups regularly are used to predict, with certainty, the end of the world because of “global boiling”. But those models are little better than a guess. We have no idea what caused historical climate change such as the Little Ice Age of a few centuries ago and the hot climate of the Egyptian period. Climate models fail on this. The bureau’s failure to acknowledge model weaknesses is unscientific. Uncertainties must be stated. If the BOM proclaims its predictions for the year 2100 are excellent, it can hardly complain when people get upset when its forecast for this afternoon turns into a dud.
Another major problem within the bureau is the section dealing with long-term temperature measurements. Most long-term measurements have been modified (homogenised), almost always making past temperatures cooler.
The BOM does not dispute it has done this, but there is a huge argument about whether it has done it in a justifiable way, and BOM has failed to release all its data about these temperature adjustments. This is inexcusable and breeds concerns about the bureau’s scientific integrity.
There is also the habit of the BOM to associate every extreme, or record-breaking, weather event with climate change. In fact, record events are inevitable every year because of the huge scale of the observation network.
But the climate section of the BOM uses record events for political purposes.
Should we have an inquiry into the BOM? Yes. But the good guys of the BOM short-term weather forecasting department need to stand up against the anti-science catastrophists in their climate department. Otherwise they deserve to be tarred with the same brush.
Peter Ridd is a physicist, adjunct fellow with the Institute of Public Affairs and chairman of the Australian Environment Foundation.
Jennifer Marohasy BSc PhD is a critical thinker with expertise in the scientific method.
https://www.dropbox.com/scl/fi/qbdnq0k800jh2ksab109u/Consensus-Has-Hysteria.pdf?rlkey=4rlir71dvkddwi078nsvgktzt&dl=0
****
Extracted from the above dropbox (by Jenn):
“The Consensus Has Hysteria. The real science is covered up very well by the non-science blanket of ‘consensus’. Fact is, all the time series used by the IPCC were affected by a dozen volcanic eruptions from 1883 (Krakatoa) to 1932 (Cerro Azul). The frequency was 4.8 +/- 4.3 years. These huge eruptions of sulfur and fine ash, larger than those
of Pinatubo and Mt St Helens, were circulated by the jet streams globally. Very few weather stations existed in 1883 but those that were operating often showed warmer temperatures than the present at the end of the Little Ice Age, and a rapid regression to the natural gravitational norm temperature before Krakatoa. IPCC has only paid lip-service to their oversight in Report No. 5 (2013-14). It is therefore extremely unlikely that anthropogenic global warming by carbon dioxide is an existential threat to
anyone.
Krakatoa and her cousins Pele, etc. provided a cool drag on regression toward the mean estimated by Arrhenius in 1906 and cited by NASA as approximately 15C in modern time. It seems we have regressed toward the mean in the 21st century but it is still not as warm as the 1860s before the AGW hypothesis of the oil age. The volcanic events were frozen into all the IPCC time series.
Additional thought – Bangladesh and Mississippi delta complexes have advanced over 130 km as sea level rose 130 m following the Pleistocene glaciation.
Dr. Francis Tucker Manns
Toronto, Canada
The BOM needs taking to with a broom. Farmers selling livestock at discounted prices in the face of anticipated drought begs the question of class action. That would be a big broom.
North Queensland is actually FAMOUS for its spectacular and deadly floods.
Entire settlements on the old goldfields, Maytown, for one, were basically picked up, shattered and swept away. Casualties unknown, as this was a wild and woolly place, at the time.
“Unprecedented” is NOT the word we are looking for.
Dorothea McKellar nailed it with, “Of droughts and flooding rains”. That transition can occur, literally, overnight. One January, out on the Thompson River, vast black-soil plains turned to swamps as we watched. From driving desperately thirsty cattle through clouds of fine dust, towards a known stretch of river containing a modicum of shallow, muddy water, to almost all vehicle movement ceasing as the soil turned to bottomless glue, in less than six hours. A part of the country where the locals strategically place steel-hulled (UV and termite-resistant) watercraft on tall sand-hills, in preparation for the next “wet”.
This is NOT “the big smoke”; it is the REAL country, where people die from not paying attention to their surroundings.
Extract from ‘Tomorrow’s Weather’ thirty years on. (p184)
‘May 21, 2020;
Mount Merapi, 7.54 d S, 110.44 d E, (near Yogyakata, Java) in continuing eruption –
A visible 20000 km cloud band from Indonesia, resulting in flooding for Northeast Queensland.
” Meteorologist Shane Kennedy said the wet weather was caused by tropical moisture drawn all the way from Indonesia across the state, being undercut by colder and drier air, forcing the tropical moisture up and forming a very thick cloud band and rain”
“It is quite an unusual set-up for this time of the year,” Mr Kennedy said.
We do have an out-of-season tropical low near Indonesia that’s causing a lot of the moisture source and the Indian Ocean in our vicinity is much warmer than usual.”
“It’s providing a lot of rich, tropical air, that’s being drawn down across Australia.”
(ABC News Report)’
Thanks for your meticulous work Jennifer. It’s much appreciated by many if not by the BOM.
I posted a link to “Hughes Fact Check” website but it seems not to come through. So I will paste the concluding remarks….
“Neither Dr Abbot nor Dr Marohasy are qualified in climate science or machine learning, and it seems they fall into error in both aspects. Machine learning can be a useful adjunct to physical models, but not if used in this way.
Conclusion
I was expecting an informed analysis of data from Dr Marohasy. But this review has shown her climate work can be criticised on a number of grounds:
Not offering evidence for statements (e.g. the problems with temperature probes; the ferocity of recent bushfires).
Stating facts and inferring fault without showing fault (e.g. the fact the BoM adjusts or omits some records from the ACORN-SAT dataset).
Use of inaccurate data (e.g. areas burnt in bushfires).
Not using all the data, without justifying the reasons (e.g. the omission of recent temperature data in the Rutherglen analysis; the assessment of temperatures in the recent bushfires; the machine learning paper).
Not referring to BoM reports that refute her claims (e.g. changes in temperature recording equipment).
Inaccurate analysis (e.g. combining the records of two stations at Wagga; using machine learning without physical parameters in assessing temperature trends).
Drawing conclusions from isolated examples while ignoring the clear trends in the full set of data.
These faults lead a number of reviewers to conclude her analysis is unreliable.
It appears that some of Dr Marohasy’s critiques of BoM data have led to the Bureau refining its approach, and this is helpful. But mostly her criticisms have little substance because of the poor quality of her analysis. BoM has a very good reputation for the quality of its work. For example this reviewer says:
“The BoM has, in my opinion, assembled the best national temperature data in the world because they use the most advanced methods to correct for known problems ….. Few people appreciate just how advanced are the methods that the BoM uses to homogenize data, or how many tests and critiques it has withstood. When I say that in my opinion theirs is the most advanced in the world, I base that on years of experience as a scientist and statistician, and on close examination of the methods used by many organizations.”
Up against such expertise, she is often left with conspiratorial claims that have no demonstrable basis or credibility.
Even if her claims about BoM were true, the overall global and Australian data clearly show a definite warming trend. Yet she and others use her critiques to unjustifiably infer there is doubt about the warming trend and climate science. The identification of minor errors, even if true, can be a smokescreen to hide the larger reality.
Critics suggest her poor analysis and unjustified conclusions result from determined opposition to the science of global warming and action on climate change. It is hard not to agree.
I conclude that none of her conclusions can be accepted without extensive checking against data using correct scientific and statistical methods.
If I can be shown to have made errors in my assessment, I am happy to make changes. I don’t wish to be unfair to Dr Marohasy.
You will get a warming trend if you have a cool start to the time series and never correct it.
https://www.dropbox.com/scl/fi/qbdnq0k800jh2ksab109u/Consensus-Has-Hysteria.pdf?rlkey=4rlir71dvkddwi078nsvgktzt&dl=0
FRan Manns “You will get a warming trend if you have a cool start to the time series and never correct it.”
Yes and no matter what model is used, if your data has that cold start and is like disease riddled parasites swimming in dog vomit of regurgitated pig poop like this classic BoM example, there is no hope at all of ever getting anything right.
The example: http://www.bom.gov.au/jsp/awap/temp/archive.jsp?colour=colour&map=maxave&year=1911&month=2&period=3month&area=nat
It is the case that 10 years ago, when I was getting started with John Abbot, using AI for weather forecasting, so many in Australia said it wouldn’t work.
Our expertise was in how we put the data sets together, we used off-the-shelf software that at the time was developed for other purposes. It was a lot of work pulling the datasets together, including because it soon became obvious to me that we had to use unhomogenised data.
It was almost as much work getting our findings published. And then when we did, we mostly received criticism including from our colleagues.
The value of AI for weather forecasting is just beginning to be appreciated, with something of a review article focusing on the big players here, https://www.netzerowatch.com/forecasting-the-weather-supercomputer-or-ai/
Anyone looking at the “weather” in the North-East USA lately?
New York; Nine Hundred days without snow. Now it is stacked metres deep with the stuff. Parts of Kanaduh likewise.
Possible “driver”? Warm, moisture-laden air moving en masse from the Caribbean up along the east coast, cooling as it travels thousands of Km over water and coastal recons, whilst heading for the Arctic Circle. NYC is closer to the Arctic Circle than it is to the Equator. Geography and WATER bodies (oceans) are the major players. And random butterflies, apparently.
Another 14.5mm in my rain-gauge here in “never going to rain again / summer-long drought” Brisbane . A lot of that arrived in the middle of the day in a twenty-minute blast of near horizontal rain and road visibility of less than 20 metres. Made more “interesting” by the rabbits failing to maintain WET condition distances and general situational awareness.
Siliggy, feel free the look at the monthly maps for the period around Feb 1911. Obviously that data is not affected by a glitch.
http://www.bom.gov.au/jsp/awap/temp/archive.jsp?colour=colour&map=maxave&year=1911&month=2&period=month&area=nat
Jennifer,
I must compliment you on your persistence. It must be a lonely job with the world’s climate science saying the exact opposite and the data being so unhelpful. From the WMO in November 2023:
“ Greenhouse gas levels are record high. Global temperatures are record high. Sea level rise is record high. Antarctic sea ice is record low. It’s a deafening cacophony of broken records,” said WMO Secretary-General Prof. Petteri Taalas.
O.T. but this new Kip Hansen WUWT post seems on the ball as far as AWS created spurious Tmax values:
https://wattsupwiththat.com/2024/01/05/rising-maximum-temperatures/
Issued by BoM on December 19, 2023… “The long-range forecast for Australia for January indicates an increased chance of above median
rainfall for parts of Queensland, NSW and Victoria, and more neutral rainfall chances across much of
the country.”
I run sheep near Avoca (Vic) and we would love more accurate rain forecasts. You should start publishing predictions for maybe one or two regions and then the truth will out. It might take a year or two but the comparison will be obvious.
One of the best resources for global temperature and precipitation data is NOAA, the latest here:
https://www.ncei.noaa.gov/access/monitoring/monthly-report/global/202311
There has been heavy precipitation and flooding in many parts of the world, including parts of Australia, but the most striking thing is how global temperatures have gone off the chart in the last 12 months. This is El Niño on top of climate charge, with a small contribution from solar cycles and one or two other things. It’s set to get more extreme in 2024 before resuming the general upward trend.
Not sure what Rob from Castlemaine knows about climate science but I do my own work and have eluded the consensus so far. The consensus has hysteria and causes hysteria.
Jennifer also does her own work.
https://www.dropbox.com/scl/fi/qbdnq0k800jh2ksab109u/Consensus-Has-Hysteria.pdf?rlkey=4rlir71dvkddwi078nsvgktzt&dl=0
Bob in Castlemaine
BoM tried that mass hat / sheath idea with their platinum thermometers and it all went wrong. Over simplified theory. Not realising part of the problem was at the other end of the long cable from the platinum thermometer, at the data logger they adjusted the sheath. The idea then more flawed with multiple problems and difficulties. So it all went wrong again and again. Years of data. Expecting a different result each time they just went on making the same mistake. Eventually settling for a poor outcome that hides well among other wide tolerances. Was watching Goulburn for the minimum part of this mass hat/sheath mess up on the mornings of July 1, and 2 2017. See my ancient comments about time constants here.
https://jennifermarohasy.com/2017/09/bureau-management-rewrites-rules/
Inner north-west Brisbane:
Rainfall for the month so far, on my little bit of drought-stricken suburbia?
66.5 mm. in nine days
I can just about hear my feral lawn growing as I walk out to the wheelie bins.
Maybe i should ditch the lawn and install ponds to grow watercress, lotus, etc. Rice?
The next couple of weeks may be “interesting’, in that allegedly Chinese curse sort of way.
Siliggy
I think the concept of using platinum resistance thermometers (PT) to record temperature is OK, they are capable of providing accurate temperature measurement. PTs can be more readily integrated into electronic weather station technology. The problem seems to be in the execution by both BoM and NOAA. I understand WMO guidelines require electronic temperature measurement response to be damped, presumably to make the readings compatible with historical mercury/alcohol in glass measurements.
The BoM does not appear to be interested in doing the comprehensive comparative testing needed to establish how data produced by their AWS, platinum based, stations compares quantitatively with older mercury or alcohol based stations during prolonged parallel operation i.e. two or three years of closely monitored side by side test station operation. One would think a major government scientific agency, with a billion dollar plus budget would be able to muster the interest to do the necessary science to demonstrate that its contemporary temperature data are genuinely comparable with its historical temperature data. What am I missing?
Bob, you are missing the fact that both BoM and NOAA are very much engaged engaged in the “necessary science to demonstrate contemporary temperature data are genuinely comparable with historical temperature data”. I think the problem is that you don’t understand BoM and NOAA’s rigorous statisitical analysis that avoids cherry-picking data to support a preconcieved warped position. Meanwhile our environment moves steadily towards unsustainability.
Bob in C. Agree. PRTDs are good for the task. 1800s tech. In the screen no heat from electronics, no adjustments and no moving parts. A bit of platinum in a factory container with wires running to it. Most traceable calibration glass thermometers go back to a platinum resistance thermometer. Missing? Among many issues, the need for two different PRTDs. One “damping” cannot match alcohol and mercury be it fixed circuitry or a mass hat / sleave. The time constants for all three change differently with humidity and wind speed. All three interact differently with the time constant of the screen. All three interactions vary with humidity and wind speed. Also i run out of text space to talk about radio and electrical noise etc & etc. There were comparisons but the mistake of the 1990s lead into this. Which could bake them in more. https://www.youtube.com/watch?v=aKN37cInO1A
Embarrassed yet?
The olde temperatures were not baked in. They were frozen in by a dozen big volcanic eruptions beginning with Krakatoa.
https://www.dropbox.com/scl/fi/qbdnq0k800jh2ksab109u/Consensus-Has-Hysteria.pdf?rlkey=4rlir71dvkddwi078nsvgktzt&dl=0
https://osisaf-hl.met.no/archive/osisaf/sea-ice-index/v2p1/nh/en/osisaf_nh_sie_monthly-ranks.png
https://osisaf-hl.met.no/archive/osisaf/sea-ice-index/v2p1/sh/en/osisaf_sh_sie_monthly-ranks.png
Thank you for bringing Elsevier’s, Advances in Atmospheric Sciences to my attention. You will be interested in a paper in their recent issue.
Cheng, L., Abraham, J., Trenberth, K.E. et al. New Record Ocean Temperatures and Related Climate Indicators in 2023. Adv. Atmos. Sci. (2024). https://doi.org/10.1007/s00376-024-3378-5
https://postimg.cc/v4b83BPv
“The increase in carbon dioxide (CO2) and other green-house gases in the atmosphere from human activities has ledto an increase in longwave radiation trapped within the Earth system, resulting in an increase in the difference between incoming and outgoing radiation at the top of the atmosphere and causing an Earth Energy Imbalance (EEI). With about 90% of the excess heat accumulated in the Earth system deposited in the world’s ocean, EEI causes rising ocean temperatures and increasing ocean heat content (OHC). Both OHC and the closely associated sea level rise (SLR) are robust indica-tors of climate change because they have larger forced signal-to-noise ratios than surface temperature change. OHC also plays essential roles in Earth’s energy, water, and carbon cycles and significantly affects human society.”
Just asking –
2023 was an El Nino year ? How much is the ‘anomaly’?
The jet streams have been wandering since the 1990s. How much does this distribute warm air and warm oceans?
Francis, J. A., and S. J. Vavrus (2012), Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, doi:10.1029/2012GL051000.
Correlation is not causation.
Fran Manns, are you suggesting that an El Niño year will warm the ocean 2,000 metres down?
I don’t know what the circulation is, and maybe no one knows. There are other inputs like thousanhds of marine volcanoes.
From SOI figures there was no super El Nino in 2023. In fact it was hardly significant. The 30 day average SOI has gone positive indicating an end of the El Nino. With the last six daily index figures being in the +20 it is possible that a La Nina has been entered. Jen as you as BOM are hopeless and have the wrong model. For Qld. SOI and IPO are strong indicators of weather for upto one year. I agree they should be looking at historical data with AI and possibly taking in planetary movements. I have found that Darwin tides have an influence on the daily atmospheric pressures at Darwin and in turn an affect on daily SOI. There is a definite 28 day pattern (from the moon). Note the daily tides at Tahiti change very little. Here is a typical tide chart
Tidetide state Time (-10)& Date Height
Low Tide 3:58 AM(Sat 20 January) 0.19 m (0.61 ft)
High Tide 11:04 AM(Sat 20 January) 0.29 m (0.95 ft)
Low Tide 5:24 PM(Sat 20 January) 0.2 m (0.64 ft)
High Tide 10:46 PM(Sat 20 January) 0.26 m (0.86 ft)
tide stateSunrise: 5:38AM tide stateSunset: 6:39PM tide stateMoonset: 1:11AM tide stateMoonrise: 2:46PM
This is on the net
In general, tidal ranges within the Pacific are small. That at Tahiti is about 1 foot (0.3 metre); at Honolulu it is about 2 feet (0.6 metre); at Yokohama it seldom exceeds 5 feet (1.5 metres); and at Cape Horn it is never more than about 6 feet (1.8 metres).
So Darwin where there are large tides is the main influence on SOI
Cyclic based forecast. Not a meteorologist. Don’t take this too seriously. Why not look at non BoM history for clues to what happened? Previous cycles point at Townsville where in 1896 cyclone Sigma hit. Newspapers record major destruction, “hardly a house escaped damage”. An old track map shows the cyclone bouncing off the coast and heading back out again to the south west. Flooding rain was guided into central Queensland and all the way down the coast further than Maryborough. More than a week later newspapers compare the Mary river flood at three feet higher than a flood in 1887. As I type there is no warning for the Mary river here. http://www.bom.gov.au/qld/warnings/flood/index.shtml
There is no mention of either the 1887 flood or the 1896 one here. http://www.bom.gov.au/qld/flood/brochures/mary/mary.shtml#FloodWarningsandBulletins
Ooops. “back out again to the south west.” That should be south east. After Townsville Sigma first went north then crossed it’s own path to head off toward N.Z.
Sliggy,
In 1893 the Great Flood of Brisbane left a path of destruction in its wake. The total rainfall in Brisbane over 8 days was about 20 inches (500 mm) and the Brisbane River rose 23 feet 9 inches (7.2 metres) above the mean spring tide and 10 feet over the previous highest flood mark (1890). Brisbane suffered approximately £2,000,000 worth of damages. The Victoria Bridge and the Indooroopilly Railway Bridge were swept away and in Queen Street, the businesses of Finney, Isles and Co, drapers, Perry Brothers, the goldsmiths, Hall Company, H. L. Davies, and Gordon and Gotch, all suffered major damage.
Brisbane wasn’t the only area hit. The countryside for miles on either side of the Mary River was devastated and the loss of settlers enormous. The Mayor of Brisbane composed the following cable for the Lord Mayor of London: “Brisbane, Maryborough, Gympie, Ipswich, Bundaberg and Rockhampton inundated by floods. Destruction of property and loss of life enormous. Relief urgently required. ” Against the wishes of the Queensland public, this message was never sent. (Western Mail, 18 February 1893 p.40)
This was a cyclone that mainly hit the Sunshine Coast. It was called the “Mooloolah Event”. At Buderim, in the month of February 1893, 1819 mm of rain was recorded at the PO. Even higher rainfall was recorded at the observatory near Maleny by the most experienced meteorologist (Inigo Jones)at the time. The record there is still the highest rainfall in Australia for a 3 day period. The 1893 total rainfall in Buderim was 3848mm but the highest was in 1898 of 3997mm when Brisbane again flooded. The lowest rainfall in Buderim was in 1902 of 519mm in the Federation drought. The floods and drought had nothing to do with CO2 and there is nothing about the present that is exceptional.
Siliggy, did you contact the BoM for the information you are interested in? They clearly not that it is available on request.
“The litigation targets two writers: Rand Simberg, analyst at the rightwing thinktank Competitive Enterprise Institute, who published a piece comparing Mann to a convicted serial child molester, and the National Review blogger Mark Steyn, who in a blogpost favorably quoted Simberg and called Mann’s research “fraudulent”
Competitive Enterprise Institute should dismiss him immediately for ad hominem.
Just filing this here:
by Peter Hannam, in The Guardian, Sunday 21st January 2024.
https://www.theguardian.com/technology/2024/jan/21/can-the-power-of-artificial-intelligence-be-harnessed-help-to-predict-australias-weather
“Kerry Plowright had his feet up and was watching TV one evening late last year when his phone warned of incoming hail.
“I was stunned when I walked out the door because there was just this roar,” he says, describing the sound of hailstones hitting roofs in the New South Wales town of Kingscliff. He had just enough time to move his cars under canvas sails, sparing them from damage.
Plowright isn’t alone in having little warning before wild weather during Australia’s seemingly relentless summer of extremes. This season may include a second tropical cyclone to strike Queensland.
The European Centre for Medium-Range Weather Forecasts’s AI model has the potential tropical cyclone Kirrily crossing the Queensland coast next Thursday night into Friday
The Albanese government has launched an inquiry into warnings issued by the Bureau of Meteorology and emergency authorities after complaints by councils and others that some alerts lacked accuracy and timeliness.
But Plowright’s case is a little different – his hail heads-up was triggered by data generated by his own firm, Early Warning Network.
Early Warning Network analyses data from radars and remote sensors to detect and issue alerts on extreme heat, rainfall and flooding. It counts local councils and big insurers among its customers.
Private businesses have long offered services based on data from BoM or agencies such as the European Centre for Medium-Range Weather Forecasts (ECMWF). But Early Warning Network is starting to test artificial intelligence models that promise to make a lot more weather information available both rapidly and at low cost.
“You have to pay a bucket load for [ECMWF] data,” Plowright says. “We don’t need now a supercomputer to go and run a forecast that will be extremely accurate up to 10 days, especially for extreme weather.”
Artificial intelligence “is going to be absolutely phenomenal with weather and ultimately climate too, once it gets there”, he predicts.
Juliette Murphy, a water resources engineer, is similarly excited. She foundedFloodMapp to give communities more time to prepare after monitoring devastating floods in Queensland’s Lockyer region in 2011 and then in the Canadian city of Calgary two years later.
FloodMapp uses machines that learn from each model run as well as traditional physics-based hydrology and hydraulic models. Even relatively basic computers can comb through “really large datasets” quickly to identify likely effects of a flood, she says.
Her clients include Queensland’s fire and emergency services. Its results complement BoM’s, helping authorities decide which homes to evacuate and which roads to close. “That’s important not least because almost half of flood deaths involve people in cars,” Murphy says.
A BoM spokesperson says the bureau had been “proactively and safely engaging with artificial intelligence capabilities for several years”.
“This area of research is one of many initiatives the bureau actively pursues to improve its services to government, emergency management partners and the community,” she says.
Justin Freeman, a computer scientist, ran BoM’s research team which was working on machine learning before he left in in late 2022 to set up his own firm, Flowershift.
Flowershift is building a geospatial model trained on existing observational data. “We would be filling in gaps around what the current forecast products are”, such as providing forecasts in remote regions of Australia or beyond, Freeman says.
“There’s a lot more flexibility to be able to explore things [outside BoM] and use technologies which are very new,” says Freeman, who still does contract work for the bureau. “We’ve got this whole new different class of models which are completely different to what [the bureau had] been running for the last 50 years.”
There are many potential uses for models that can analyse data cheaply and then supply localised information. Farmers, for instance, could ask, “Should I spray my crops this week?” and be told why or why not, Freeman says.
“It hasn’t been that long that we’ve had access to something like ChatGPT,” he says. “Look forward like another two years, five years – it’s just going to accelerate and get better and better.”
Some BoM and climate researchers, though, caution how much AI-based models, such Google’s GraphCast or Nvidia’s FourCastNet, can improve on numerical models that churn out a range of probabilities.
‘Very scary’: Mark Zuckerberg’s pledge to build advanced AI alarms experts
“For ‘simple’ weather forecasting and for downscaling physical model data I reckon [there’s] massive potential,” one bureau scientist says. “For warning us of real dangers when the atmosphere gets violent, I’d be very cautious.
“And with climate change, we need to better understand things that are well outside the norm.”
Sanaa Hobeichi, a post-doctoral researcher at the ARC Centre of Excellence for Climate Extremes, says there are still benefits despite the limitations.
Existing climate models typically offer only “coarse” resolutions, such as estimating rainfall changes over areas 150km by 150km. In Sydney, for instance, a model that size would capture the city, mountains and a lot else and so be of limited use.
Google’s GraphCast forecast model has a resolution down to 28km by 28km, while Hobeichi says some AI can model just 5km by 5km.
A challenge, though, is that machine-learning techniques inherit and potentially extrapolate imperfections of the traditional models they train on.
Jyoteeshkumar Reddy Papari, a post-doctoral CSIRO researcher, notes that the ECMWF was initially sceptical of AI but has lately started its own experimental model. It is also displaying several others on its website, including Google’s.
“Countries that don’t have good meteorological organisations are relying on these machine learning models because they are super easy to learn and are publicly available,” he says. “So some of the African countries are using these forecasts.”
Google researchers last year claimed GraphCast “significantly outperforms the most accurate” operational systems in 90% of 1380 targets. Tropical cyclones, atmospheric rivers and extreme temperatures were predictions it made which were better than traditional models and improvements are ongoing.
“One particular example we often mention was Hurricane Lee, because it was the first time that we observed in real time how GraphCast was predicting a hurricane trajectory that originally differed from the traditional systems, and eventually was shown to be the right trajectory,” said Alvaro Sanchez-Gonzalez, a Goggle researcher.
“It was detected in real time and it was verified by independent sources.”
Current tracking of the potential cyclone in the Coral Sea – to be named Kirrily if it forms as expected by Monday – will also be monitored to see how models compare.
ECMWF’s machine learning coordinator, Matthew Chantry, says AI models are “a very exciting avenue as a companion system for traditional forecasting” although the latter retains some advantages.
“Tropical cyclone intensity estimates are a good example,” he says. “It’s an open question whether these flaws are maintained as the technology matures – it is still very early days.”
Authorities act based on the probabilities calculated by traditional models but that needs a very large supercomputer. “With AI forecasts, this is dramatically reduced, with some estimates suggesting a 1000-times reduction in the energy to make a forecast. Cheaper systems could therefore be a force for equality.
“This reduced cost could also be invested into larger ensembles, meaning that we have a better idea of low-probability but extreme events that could occur.”
And as for predicting effects of a heating planet?
“The problem is significantly harder than weather forecasting, with less data,” says Chantry. “That said, in a changing climate, where evidence suggests an increase in extreme events, then any help with predicting these events has significant value.”