Regression to the mean is all nice and good, it may be applicable for the next couple decades lumped together, but it's not of any use to attempt to predict the outcome of a single season.
It is not just NOAA that predicted an above average season, everyone that I'm aware of that issues a seasonal forecast did. Those guys are scientist, not politician. Their funding doesn't depend on AGW, and if it did, they are aware that there is no consensus on the impact of global warming on the frequency of tropical cyclones. There is demand for seasonal forecasts, yes they are still low skill, but they are not no skill.
The big factors in the seasonal forecast this year were a warm Atlantic and an expected neutral phase El Niño which both of which positively correlate with tropical storm frequency. Despite neutral El Niño, there has been more shear which is unusual and has prevented storms from strengthening much. Dry air has also been a problem. The scientists producing the forecasts will learn from it.
The season is not over, October can be very busy. Yes, at this stage it would be shocking if the higher end of those prediction range were reached, but the lower, not so much.
We're talking about the prediction of the future of a complex system. While the forecasts give hard numbers (temperature forecast) or narrow ranges of outcome (precipitation amounts), it's because that's what the general public seems to understand. Those "deterministic" weather forecast should be understood to have an implicit confidence interval.
If I forecast a high of 25 degrees and it turns out to be 26, am I wrong?
I'd really like to see your Dr Loranze reference. Most likely he's talking about meteorolgy. Predicting precise weather 7 days ahead is indeed very tricky.
But talking about the global weather 50 years from now is climatolgy. You don't care about every storm cloud around. The things of interest are high energy phenomenon like oceanic current, the global atmospheric circulation. the variation of the sun's radiative output and the magnitude of the greenhouse effect. Global climate prediction is a very complex problem, but the most important factors are less chaotic then local weather and can be predicted with some accuracy for long periods.
Slashdot Mirror
Your post is wrong and offensive, good job.
Regression to the mean is all nice and good, it may be applicable for the next couple decades lumped together, but it's not of any use to attempt to predict the outcome of a single season.
It is not just NOAA that predicted an above average season, everyone that I'm aware of that issues a seasonal forecast did. Those guys are scientist, not politician. Their funding doesn't depend on AGW, and if it did, they are aware that there is no consensus on the impact of global warming on the frequency of tropical cyclones. There is demand for seasonal forecasts, yes they are still low skill, but they are not no skill.
The big factors in the seasonal forecast this year were a warm Atlantic and an expected neutral phase El Niño which both of which positively correlate with tropical storm frequency. Despite neutral El Niño, there has been more shear which is unusual and has prevented storms from strengthening much. Dry air has also been a problem. The scientists producing the forecasts will learn from it.
The season is not over, October can be very busy. Yes, at this stage it would be shocking if the higher end of those prediction range were reached, but the lower, not so much.
For some definition of wrong.
We're talking about the prediction of the future of a complex system. While the forecasts give hard numbers (temperature forecast) or narrow ranges of outcome (precipitation amounts), it's because that's what the general public seems to understand. Those "deterministic" weather forecast should be understood to have an implicit confidence interval.
If I forecast a high of 25 degrees and it turns out to be 26, am I wrong?
I'd really like to see your Dr Loranze reference.
Most likely he's talking about meteorolgy. Predicting precise weather 7 days ahead is indeed very tricky.
But talking about the global weather 50 years from now is climatolgy. You don't care about every storm cloud around. The things of interest are high energy phenomenon like oceanic current, the global atmospheric circulation. the variation of the sun's radiative output and the magnitude of the greenhouse effect. Global climate prediction is a very complex problem, but the most important factors are less chaotic then local weather and can be predicted with some accuracy for long periods.
Time Cube is a lie.
It limit you to 4 days.
Why? When you can have an infinite number of days with a Time Sphere.