Alternative hypothesis that also fits the evidence: You look bad if reality turns out worse than your forecast. You look less bad if reality turns out better than your forecast, thus there is a built in incentive to make forecasts more pessimistic than is justified.
Imagine if 10in were forecast and then NYC actually got 2 feet. Then everyone would be complaining that the city wasn't adequately prepared for the 14 extra inches they received and would blame the forecasters for all the economic consequences of not being prepared.
If I'm understanding the article correctly, this seems correct. Of the many models almost everyone chose to emphasized the models with the more extreme output. Different parties had different motivations: selling news, avoiding political blame...
Interesting things happen when science meets human nature. Science rarely wins especially if there is uncertainty.
> Science rarely wins especially if there is uncertainty.
I agree 100%. One of the most interesting books I've read lately has been The Black Swan by Nassim Taleb, which deals with this at length (ostensibly it is about unpredictability in financial markets, but the implications are far broader than that).
>Imagine if 10in were forecast and then NYC actually got 2 feet.
i'm imagining it, and not really seeing the problem. 10" and 2' are both enough snow that you probably don't want to drive in it unless you have to. It might make a difference for the snow plow operators or the skiers, but for the average person the only thing they need to know is "should i plan to drive to work in the morning" and the weather forecasters correctly predicted "no".
Agreed if you can only report one figure, but the point of the article is that forecasters did only report one figure when they should have made it clear that there were competing models with very different predictions.
Imagine if 10in were forecast and then NYC actually got 2 feet. Then everyone would be complaining that the city wasn't adequately prepared for the 14 extra inches they received and would blame the forecasters for all the economic consequences of not being prepared.