Weather, Climate and Model Madness
Weather or climate? It pays to know the difference before we slaughter our economy on the climate alarm altar.
“Weather” describes atmospheric conditions at any location – temperature, humidity, clouds precipitation and winds. Every place has its own weather which depends on the time of day, the season, the latitude, local topography and the surface temperature of the nearest ocean.
Meteorologists need a good knowledge of weather records, atmospheric physics, geography, oceanography and solar cycles. Weather is mainly about wind – is it hot or cold, moist or dry, strong or weak? Surface atmospheric pressure gradients control wind strength, direction and temperature, and are valuable tools for short-term forecasting.
Longer-term weather forecasters will find value in studying sun spots and El Nino episodes in the oceans. Few weather-men see any value in measuring or forecasting atmospheric CO2 to help forecast the weather.
“Climate” is defined as the thirty year average of weather at that spot. One week of bad weather is not evidence of climate change, no matter how often the ABC claims that.
And adding the word “Climatology” to the name of the Bureau of Meteorology does not magically convert weather men into climatologists.
To determine climate trends requires centuries of reliable weather records. This is why geologists feature so prominently in determining past climates by mapping earth’s crust and collecting deep core samples in ice sheets, ocean and lake sediments and crustal rocks. (And it explains why climate alarmists alter past temperature records to create spurious warming trends.)
A canny and persistent mathematician/engineer Milutin Milankovitch was one of the first to suggest that changes in various solar cycle orbits and axial tilts cause changes in Earth’s climate. He spent years carefully calculating (by hand) how such changes caused changes in solar heat received by the Northern Hemisphere landmass.
He speculated that this caused the advance and retreat of the great northern ice sheets. Since then dozens of geologists, palynologists, astronomers and engineers have confirmed the reality of the Milankovitch cycles.
‘Models” provide the comedy act in the climate circus. Using taxpayer funds and massive computers they build super-complex models designed to prove that global temperature will rise dangerously because of human production of carbon dioxide. These models supposedly prove that the world faces an unprecedented episode of imminent and irreversible global heating.
Climate models have three features.
Firstly, they assume that carbon dioxide in the atmosphere is driven by human activity, and that CO2 drives global temperature. They calmly ignore the moderating effects of oceans, the unmeasured effects of volcanoes and the declining effects of extra CO2.
Secondly, they try to write formulae for the myriad of factors that drive the weather. Then these computers spit out their estimates for “average global temperature” – a bureaucratic invention – nothing lives or grows in “average global temperature”.
Thirdly, these models have only one valuable feature – they are known to be consistently wrong.
The assumptions are suspect, the relationships are far more complex than the models assume, and their scary forecasts are worthless. (see results below).
Image: John Christy / UAH
Even if the modelling scaremongers were correct, we would expect a greener earth as warmer temperatures, more carbon dioxide plant food and more rainfall encourage all types of food production, grass growth and forest expansion.
However the Milankovitch solar cycles have not suddenly stopped. The warming top has passed and colder times are ahead. Oceans retain heat longer than land. Warm oceans and cold land can cause massive precipitation of snow, advancing ice sheets and a cold hungry world.
When that happens there will be no electricity from iced-up wind, solar and hydro generators and electric vehicles will soon run out of juice. There will be battles for ice-proof energy like nuclear, coal, oil and gas and a scramble for old diesel generators, trucks and cars.
Header image: US Climate Reference Network
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Alan
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The problem is with the definition of climate. An average of any weather related variable over a 30 year period, especially temperature which is averaged over the entire Earth, is utterly meaningless. What is it that concerns us, not the average, it is the extremes we worry about
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Boris Badenov
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In my little corner of this rock, Silicon Valley, back before it became the over crowded crap hole it is, we had, on average 5 days over 100º, we had about 2 thunder storms during the summer, once in a while we’d have a few hail storms and rarely snow. Since we became a frozen rock in the 80’s and are now under 40′ of water from the rising oceans, our population has increased 4x and yet, we still have the same number of hot days, fewer hail storms and no snow on the valley floor, but more snow in the hills around us. The beach is still right where it was. This whole globull climate crap is of people that don’t know history.and are too stupid to do any research.
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Koen Vogel
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The reason the CMIP5 climate models are very bad at predicting future results is a direct result of the IPCC Optimal Fingerprint Analysis method. The method requires 2 climate model runs: one for “natural” forcings only and one for “anthropogenic” forcings only (or anthropogenic plus natural). The two climate model runs – their temperature realizations – are then individually scaled (multiplied by a factor) and added. Their sum is then further multiplied by a third scaling factor. The method is not based on physical reality:
1) you cannot add temperatures. A 1 later bucket of boiling water added to a 10 C pool does not result in a 110 C pool, or average to a 55 C pool
2) The scaling factors (fudge factors) are determined using a method that basically throws out pre-1960 data, that is pre-1960 temperature variability is dismissed
3) The scaled climate model output needs to match the historical temperature record, which it generally does for the post-1960 GMST data, albeit using fairly large scaling factors. The models generally cannot match the 1901-2010 data: the GMST record shows a 0.5 C rise between 1901-1943, a 0.3 C drop until the mid-1960’s, and a 0.6 degree rise afterwards. To match this the IPCC require unrealistic scaling factors, for example scaling factors of 10 or more (order of magnitude difference), or negative scaling factors. Needless to say the physical model underlying the climate models cannot be right if its model result is 1000% off, or if you need to subtract one temperature from another: adding temperatures is dodgy enough but subtracting one positive temperature forcing effect from the other is absolutely junk science.
The end result is that the climate models predict that GMST rises lock in step with post-1960 CO2 trends, which results in overproduction of future results, as any crossplot between RFGHG due to a CO2 increase has no correlation whatsoever (r2 = 0) with GMST increases for the 1901-2021 period. You can test this yourself using the NASA/NOAA annual data.
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