Warm Oceans Turned a 3-Inch Forecast into a Record NYC Blizzard
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Confidence has increased enough to warrant winter storm watch issuance for the entire CWA. Latest 12Z guidance coming into better agreement on potent northern stream shortwave energy diving SW from wrn Canada and the northern Plains into the plains and mid Mississippi valley by Sunday morning, phasing with southern stream coming out of the SW states and Mexico to carve out a deep closed low aloft over the Mid Atlantic and induce rapid cyclogenesis off the Mid Atlantic coast, with the surface low bombing out from from 1008 mb off the N Carolina coast Sunday morning to 970-975 mb near 38-39N/71W by Monday morning, then passing just outside the 40N/70W benchmark Monday afternoon, GFS still more intense and closer to the coast than the ECMWF, with its heaviest snow bands directly over the area as opposed to just offshore. NAM and SREF have both trended toward a heavier snowfall scenario as well, which has been a good signal in past heavy snowfall events.
Snow should start Sunday morning, and may mix with rain at times especially in the NYC metro area, NE NJ and western Long Island. Then as precip intensity picks up later in the day Sunday p-type should become all snow throughout. Heaviest snow looks to fall from late day Sunday into Monday morning, then snow tapers off Monday afternoon.
Greatest likelihood of seeing 6+ inches will be along the coast, especially eastern Long Island where up to a foot of accumulation is possible. Winds will also be strong Sunday night into Monday morning especially along the coast as the sfc low deepens, with blowing and drifting snow and some downed tree limbs as winds gust to at least 40-45 mph, and possible blizzard conditions in Suffolk, and near blizzard conditions elsewhere along the coast including NYC. NAM/GFS both signal potential for wind gusts to 60 mph late Sunday night into Monday morning, though these winds can sometimes be overdone in heavy snow events. If trends for heavy snow and strong winds continue to increase and expand northward, the potential for blizzard conditions could encompass all coastal areas.
- If I play roulette in a casino today, I might win big, break even, lose a little or lose a lot. If I play roulette in a casino every day for a decade, I can be nearly certain I will lose a lot.
- Consider an ant walking on a rough stone road built up the side of a hill. If you look at the ant at any particular second, its body might be pointing up (head higher than tail) or down (vice versa) or level, depending on what particular angle of rock it's on at that time. But measured over minutes its likely to be at a greater altitude above sea level than where it started. Measured over the hours it takes to get from the bottom to the top, it's definitely higher.
- A random day of the year (pick from 1-365) in England might be sunny or rainy, but the chances of it being sunnier are higher if the day picked is in the summer.
The point is that there's a tremendous amount of noise in short-term measurements which tend to smooth out over longer term where trends are more clearly revealed. That's the counterpoint to your argument and the reason why climate prediction is not the same as weather forecasting. Going back to the casino analogy, climate prediction is looking at your bank balance over decades; weather forecasting is deciding how to bet on a particular poker hand.
(And finally, we actually kind of do mostly know what's going to happen tomorrow, but not a week out; that's not the point you're making though.)
Having people making the same stupid comment after 3 decades needs to be handled more critically
Let’s go back 40 years and listen to the warning:
Weather forecasts are generally accurate about 90% of the time for a five-day forecast and around 80% for a seven-day forecast. Forecasts beyond ten days are only correct about half the time.
Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.
Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?
That’s not to say climate change isn’t real, but your analogy doesn’t make sense.
We don’t have an accurate model for weather, so we can’t predict it well.
I don’t see a reason to assume our model for climate is accurate, either.
Our “models of climate change” have regularly been falsified at this point. It is absolutely unknown how much “climate change” is attributable to humans right now.
Nor is it actually known what the net favorability of mild warming might be…including the possibility of mitigating the next Ice Age!
Flipping coins: no predictive models, very definitive statistics Weather: +/- 2 week predictive models, 100 years of measurements getting more definitive each year where trend are headed
Compare with another topic like, say, evolution. Here outcomes are testable and verifiable because we can observe the theory at work by watching micro-ecosystems, or small animals with fast reproductive cycles.
Meteorology is short term accurate based on a linear regression of data points from historical data. Deviation like "warming" or "cooling" are relative descriptions of how closely aligned one theory is to the line, and how far back the specific model goes along with the number and quality of relevant factors you want to look at.
No matter which model you go with, you're proving the accuracy of a math function at matching historical data, and then hoping that it will match the future. And as we know, none of them match the future very accurately, which tells us there's something wrong with the theory.
This is only slightly better than day trading in the stock market. And much like the stock market, everyone thinks they know better than everyone else but statistically, most fund managers and professional stock callers underperform the market. They earn by selling you on the idea that they have the next model that finally DOES make accurate predictions. They tell you that they know that because this new model matches the historical data more accurately. No shit. Because there's more data now in a growing set of data. So the most recently calculated linear regression is the most accurate.
But we don't know how it works. That's the key here. More data, doesn't mean the theory is better. More accuracy in making predictions about the future, on the other hand, is a strong indicator, and maybe the only indicator, that something is worth believing in. That is to say, it's more likely to be true.
Making overzealous claims about how much we know is not science, it's ignorance. Let's help interest people in science by being cautious about what we claim to know for sure. At least don't claim to know the next 200 years, until we can at least make accurate predictions beyond the next few days.
I majored in biochem with a lot of extra classes I took for fun on environmental chemistry. You?
"Unknown unknowns" aren't the reason weather forecasts are inaccurate.
Weather is path-dependent. Small changes to starting conditions or minor differences between modeled and actual conditions shortly after the simulation begins lead to large differences by the end of the simulation. Errors propagate and magnify.
Over large time periods the errors average out.
Apparently there was just a hurricane in winter and it struck New York.
I guess not everybody is going to have any understanding at all, you proably had to be there :\