Trying to understand QBO may lead to madness, if the plights of Richard Lindzen (Macbeth) and Timothy Dunkerton (Hamlet) are any indication. It was first Lindzen — the primary theorist behind QBO — in his quest for scientific notoriety that led to lofty pretentiousness and eventually bad blood with his colleagues. Now it’s the Lindzen-acolyte Dunketon’s turn, avenging his “uncle” with troubling behavior
A WaPo article based on this research https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EA001223Continue reading
I am a physical oceanographer who knows nothing about the Chandler wobble, is only slightly familiar with the QBO, but is a longtime expert on ENSO.
To be blunt, trying to shoehorn ENSO into a periodic tidal framework stretches reality to fit someone’s preconceived theory. Only the most motivated reasoning can believe this.
… (more stuff)
I am sorry to have wasted an hour on this.
Billy Kessler, NOAA/PMEL, SeattleInteractive comment on Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-74,
Billy also wrote this on his web site (emphasis mine):
4. An idea for a science fair project.
Requested by a parent.
Here’s an idea. This experiment is similar to what actual scientists are doing right now.
The project is to construct some forecast models of El Niño’s development over the next few months. We don’t know what it will do. Will it get more intense?, weaken?, remain strong?, and if so for how long? These are the subject of much debate in the scientific community right now, and many efforts are under way to predict and understand it.
The models would be forecasts made using several assumptions, and the main result would be graphs showing how the forecasts compared with actual evolving conditions.
One model would be called “persistence”. That is, whatever conditions are occurring now, they will continue. Surprisingly, persistence is often a hard-to-beat forecast, and weather forecasters score themselves on how much better than persistence thay (sp) can do. A second model is continuation of the trend. That is, if the sea surface temperature (SST) is warming up it will continue to warm at the same rate. Obviously that can’t go on forever but in many ways a trend is a good indicator of future trends. A third model is random changes. Get a random number generator (or pick numbers out of a hat). Each day or week, use the random numbers to predict what the change of SST will be (scale the numbers to keep it reasonable). Those are three simple models that can be used to project forward from current conditions. Essentially that’s what weather forecast models do, just more sophisticatedly (see question 13). Maybe you can think of some other ways to make forecasts (if you get something that works, send it in!)
Choose a few buoys from our network in different regions of the tropical Pacific (for example, on the equator, off the equator, in the east, and the west). Get the data from our web page (click for detailed instructions to get this data). Make and graph predictions for each buoy chosen for a month or two ahead, then collect observations as they come in (the data files are updated daily). Graph the observations against the three predictions. My guess is that each model would be successful in some regions for some periods of time. Other extensions would be to compare forecasts beginning at different times. Perhaps a forecast begun with September comditions (sp) is good for 3 months, but one begun in December is only good for one month. Etc.
Another simple project is to determine how significant an effect El Niño has on your local region. Do this by gathering an assortment of local weather time series from your region (monthly rainfall, temperature, etc) (available at the web pages of the National Weather service). Then get an index of El Niño like the Southern Oscillation Index (see Question 17 for a description and graphic, and download the values at NOAA’s Climate Prediction Center. The specific data links are: values for 1951-today and 1882-1950. Note that the SOI monthly values are very jumpy and must be smoothed by a 5-month running mean). Compare the turns of the El Niño/La Niña cycle with changes in your local weather; this could either be through a listing of El Niño/La Niña years and good/bad local weather, or by correlation of the two time series (send me e-mail for how to do correlation). You will probably find out that some aspects of your local weather are related to the El Niño/La Niña cycle and some are not. Also that some strong El Niño or La Niña years make a difference but some do not. This reflects the fact that, far from the center of action in the tropical Pacific, El Niño is only one of many influences on weather.
If your (sp) are pretty good at math and computer programming (at least 8th-grade math), then I have a more advanced project that you can find here.FAQ from http://faculty.washington.edu/kessler/occasionally-asked-questions.html#q4
shorter: “your thay”