Posted on 10/12/2022 4:49:31 AM PDT by Oldeconomybuyer
A new study led by Graham Taylor, a Ph.D. student in Portland State's Earth, Environment, and Society program and Paul Loikith, associate professor of geography at PSU, tested how well climate models represent large-scale weather patterns over the Pacific Northwest.
Since all computer models have different strengths and weaknesses based on differences in physics, scientists often use the output from many different climate models to assess projections of future climate change. For this study, the researchers used data from the state-of-the-art sixth phase of the Coupled Model Intercomparison Project (CMIP6) to test how well 26 different climate models could simulate the range of large-scale patterns of atmospheric circulation (like wind and pressure) found over the Pacific Northwest.
To test the models, the team used a machine learning technique called self-organizing maps to group daily weather patterns simulated by the climate models into a set of 12 categories. They did the same for historical observed weather data. They then compared the two sets of data to see how well they lined up.
The researchers found that the climate models generally simulated the observed wind and pressure patterns very well and that the temperature and precipitation patterns created by the models closely matched the correct patterns found in the historical data.
These results are important because they suggest that current climate models represent large-scale weather patterns reasonably well in the Pacific Northwest.
(Excerpt) Read more at phys.org ...
Right up until they don’t. Weather and climate are so complex that all models will become GIGO.
Lying Democrat scum.
We will continue to tweak the code until we get the desired results.
This coming from people getting large grants to prove climate change.
scientists often use the output from many different climate models to assess projections of future climate change>>>>>>>>>
So the lefty greenies can justify imposing high carbon taxes on farmers and truckers who do not use electric trucks and tractors by 2025?
Eff them and their models.
I want my own Red Camaro SS, the open road with low gas prices and a pulsing V8 under the hood.Figure that into their model.
Eff their models and the horses they rode in on.
> We will continue to tweak the code until we get the desired results. <
Yep. All you gotta do is add “normalization factors” and you can make 1+1 = 325.
Zero conflict of interest because scientists never lie...
No mention of how accurately the models reflect the impact of CO2 and other GHG. In fact the article doesn’t mention whether GHG are represented in these models at all.
And yet the article suggests we should trust these models to predict climate change.
Anybody could watch an episode of Jeopardy...then re-watch the same episode and get all of the answers right.
But I thought climate science was settled science. Are you saying it isn’t?
I am a man made climate change denier.
I will say 10 Hail Leo’s and 4 Our Gore’s.
Isn’t this generally known as forecasting the weather?
We know how accurate that is from event to event, week to week.
And they still get to keep their grants.
In the Southeast the NWS couldn’t hit a forecast if their lives depended on it. Even the local Meteorologists are talking about how they don’t trust NWS modeling anymore. In one forecast this past summer the NWS predicted a high temp of 101F with heat indexes in excess of 115F. Twelve hours later the actual high temp was 91F and even the heat index didn’t reach their projected high temp. NWS models ALWAYS skew to the warm side. If you need another example of how poor NWS modeling is, have a look at the hurricane Ian forecast. The European models nailed it 5-6 days out. The NHC models were off by 200+ miles during the same time period.
Models are always an abstraction. If they were not an abstraction then they would be the real thing. Nature has an infinite number of variables. That makes it impossible to account for every combination of outcomes using a computer model.
Just take Hurricane Ian for instance. Meteorologists were just complaining how no model was able to predict landfall. Journalists , who are trained to write at a sixth grade level and don’t know excrement about science, mathematics and modeling, probably didn’t quite understand or report that models of Ian converged on Ian’s landfall as Ian made landfall. The timeframe was shortened and the data became more accurate, or should we say, relevant.
Knowing this, a question needs to be asked about the climate models for the Pacific Northwest. At what point in time did the models become accurate? One year? One month? One day? One minute? When was there enough real data to make an accurate prediction?
For instance, how accurate was the prediction for temperature, since that is easy for people to understand compared to pressure. (Although the article doesn’t discuss temperature) Within 1C? 3C? That’s a huge margin of error when these quacks talk about a time horizon of decades and the effects of 1.3 C increase in temperature. Understand that temperature prediction is just one result that comes out of a model. There are many other results. How many of the results were accurate at the same time and what was the degree of accuracy?
If we have five different types of results that are all slightly inaccurate, then the results compound the error. What about ten, or fifteen? That’s just trying to interrupt the models predictions.
There is another sticky problem. What is the degree of accuracy of the model’s inputs. How many measurements are there? How many locations do the measurements come from? At what altitude in the atmosphere do the they come from? There are an infinite number of combinations.
Try this experiment. Run around your house with an infrared thermometer and take a bunch of temperatures. Point it at the floor, ceiling, walls, and different surfaces. You’re dealing with a closed system. You will probably find that you will get quite a few different readings. Thus, this is to say that in the open system of nature that it is highly probable that the range of input measurements for model will vary significantly. That changes the output of models.
Just remember, “progress” is required to be shown to get more funding and grants. This article does not specify any measurable degree of accuracy. The ignoramuses in government who are blinded by communist utopia have no understanding of quantitative measurement, statistics and error. They are impressed by qualitative narratives that align to their “studies” degree indoctrinations.
They named it the Blind Squirrel Model Mk 1.
When they can ACCURATELY tell me on June 1 when and where hurricanes will make landfall that year, I might start believing them.
Contrast weather/climate prediction with the solar eclipse of 2016. They told us when and where the total eclipse would occur, accurately in both time and location. They demonstrated an understanding of the system.
Climate “scientists” have yet to demonstrate an understanding of the climate systems.
They had two choices: use the model and prove it, or talk about the model and say it’s accurate.
They chose the latter. This, of course, is bullsh!t.
It is not just the number of variables; it is also the underlying equations.
The atmospheric models rely on non-linear equations. Even non-linear equations with only three variables can be unpredictable. With these equations, small errors are amplified, such that two simulations starting with small differences will have diverging results after a period of time. And one can never measure to the accuracy needed to make truly long-term predictions.
What amazes me is that the climatologists should know this, unless they really don’t understand the mathematical nature of the equations they are trying to solve.
Another doctoral student gets his dissertation approved. Starts with 26 different models, none of which agree with each other and none of which make accurate projections for the future. But with enough tinkering of the parameters, can replicate known results from the past. For a local/regional weather record. Isn’t science wonderful. Color me unimpressed.
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