Comment by aflukasz
10 months ago
> In 1981, weather disasters caused $3.5 billion in damages in the United States. In 2023, that number was $94.9 billion (https://www.ncei.noaa.gov/access/billions/time-series).
Does that surprise someone? I think I would not have guessed this growth to be on such a scale. The chart suggests that severe storms are the main culprit.
Not at all. Look at the growth in human buildings in the most at-risk areas and you’ll see why that number is so big now. It’s only slightly due to an increase in severe weather event frequency / severity.
Indeed, and not just building more in more at-risk places but also the cost of building materials, construction labor and code compliance requirements have all generally increased more than baseline inflation. Factors like these tend to greatly increase recent estimates vs historical.
I read a paper a few years back which dove into how the data sources for weather damage assessment have changed a lot over the years. Much of the increase is due to more complete reporting and changes in categorization. Also, nowadays more things are insured and modern IT has made gathering the insurance reporting far more exhaustive. Plus local, state and federal agencies responsible for relief and/or recovery are gathering and reporting increasing amounts of data with each decade since the 70s (in part because their budgets rely on it). Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then.
Although I have no experience related to weather science, I remember the paper because it made me realize how many broad-based, multi-decadal historical data comparisons we see should have sizable error bars (which never make it into the headline and rarely even into the article). Data sources, gathering and reporting methods and motivations are rarely constant on long time scales - especially since the era of modern computing. Of course, good data scientists try to adjust for known variances but in a big ecosystem with so many evolving sources, systems, entities and agencies, it quickly gets wickedly complex.
> Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then
This is definitely part of it. Another part is that people live in more at-risk regions now than in the past (Florida is a great example, population has more than 10x'd since 1950).
Ultimately, the way we think about it is no matter what the underlying cause, weather-related damages could be significantly reduced with better data/forecasts
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Definitely a bit of cherry picking. Just 2 years later in 1983 the damages were $36 billion, but that wouldn't make quite as scary of a statement for the website.
One detail here is that 1981 dollars aren't 2023 dollars, so to compare they need to be adjusted.
Using [0] $3.5 bn in 1981 would have been worth $11.7 bn in 2023.
Another comment [1] noted (but unfortunately didn't cite) that two years later the damage was assessed at $36 bn, or $110 bn in 2023 dollars.
[0] https://news.ycombinator.com/item?id=41295116
No, they don't need to be adjusted. The linked website has already adjusted for CPI. There's even an option to turn on/off adjusting, and it's on by default. I didn't cite because this is using the same data / website as the original claim.
Yeah, it's a shocking number, and it's just for the US. The global estimates for severe weather are even higher [0], and in places with less infrastructure, the costs are usually more heavily weighted toward human life lost.
Obviously what we're doing can't prevent severe weather from happening, but even very small improvements in accuracy and timelines can have a massive beneficial effect when a disaster does happen. My cofounders and I are all from Florida, so hurricanes are the most visceral examples for us. When hurricanes hit, there are always issues along the lines of "we didn't have the right resources in the right places to respond effectively." Those types of issues can be combated with better info.
[0]: https://www.statista.com/statistics/818411/weather-catastrop...