How can we call something a thousand-year storm if we don’t have a thousand years of climate observations?
The article below gives the official NOAA answer to that question. It is a bit heavy with statistics but it makes a fair point given its assumptions. And the main assumptions is that the recent record is representative of the longer record.
And that assumption is wrong. We know that the longer climate record had much more extreme events than the recent record has. There were warm periods, little ice ages etc. The recent record CANNOT be used as an estimate of the longer record because it is not representative of the longer record.
What they say is not guesswork. They are not just making things up. It is conventional extrapolation but the conditions for accurate extrapolation are not met.
I give the text minus graphs below just to give readers an idea of the argument. It is a well set-out and respectable argument but it relies on a false premise. And, sadly, they must know it is a false premise. The writer is capable of good academic work but has prostituted his work to prove a falsehood. We CANNOT accurately know things about the climate of the past for which we have no historic data
The summer of 2016 overflowed with extreme rain events. Here at Climate.gov, we’ve written about two of them: the June floods in southern West Virginia and the mid-August floods in Louisiana.
After the historic flooding in West Virginia in June, the National Weather Service said that in parts of West Virginia, 24-hour rainfall amount—more than 10 inches in some places—were a thousand-year event. We often do not have observations that go back 100 years, let alone 1,000. So how do scientists figure that out? The answer lies in statistics.
Precipitation, rain, West Virginia, flooding
An "early glimpse" of 24-hour rainfall totals from storms over West Virginia on June 23, 2016, based on PRISM data from Oregon State University. "Early glimpse" data may not include data from all stations in the reporting network, and totals should be considered preliminary. Even the preliminary totals are enormous, however, with up to 8 inches of rain in many areas of southeastern West Virginia. Map by NOAA Climate.gov.
Dinosaurs and data
Estimating the size of a thousand-year event using a much shorter history of observations is like how paleontologists can take an incomplete collection of fossilized Tyrannosaurus Rex bones and turn them into a picture of what T-rex probably looked like when alive. The climate “bones” are all the observations we have. Since we have an admittedly incomplete set of weather observations, we have to use what we’ve got to create an image of the actual climate “dinosaur.”
Let’s work through it with a real-life example. I have compiled over 80 years’ worth of daily rainfall observations from the Beckley VA Hospital in West Virginia, near where June rains were so extraordinary. First, I eliminated any year with more than 10 day of missing data. Next, I pulled the highest daily rainfall amount that occurred in each year (1). Some years clearly have larger daily rainfall maximums than others.
Annual maximum precipitation, rain, bar graph
Annual maximum daily precipitation totals from 1909 to 2015 at a weather station located at Beckley VA Hospital in West Virginia. Years where more than 10 days of precipitation data were missing are excluded. NOAA Climate.gov based on data from the National Centers for Environmental Information.
To figure out how rare a particular rainfall event was, we need to understand the range of the data. We’ll start by putting the values in order from smallest to largest.
Annual maximum precipitation totals (inches) sorted from smallest to largest for 82 years at Beckley VA hospital in Beckley, West Virginia. The annual maximum precipitation total exceeded 4 inches in only two of the 82 years. NOAA Climate.gov map based on station data from the National Centers for Environmental Information.
Ordering the data from lowest to highest allows us to see the spread in totals but doesn’t help us figure out what is the most common daily rainfall maximum. For that, we need to sort the values into bins defined by rainfall amount (a bin for 0 inches, 0-0.25, 0.25-0.5 inches etc), like sorting clothes into piles based on size. It is at this step, that we can begin to see if there is a pattern.
histogram, precipitation frequency, heavy rain, extreme, West Virginia
A histogram of annual daily maximum precipitation totals for Beckley, West Virginia. There are 82 years in total. Precipitation totals are sorted into 0.25-inch bins. The most common bin, with 18 events, represented daily precipitation totals between 2 and 2.25 inches. 80 of the 82 years had precipitation amounts less than 4 inches. NOAA Climate.gov figure based on data from the National Centers for Environmental Information.
Certain piles have more items of clothing in them than others: we have more mediums than extra-larges so to speak. It is clear that some yearly 24-hour rainfall maximums occur more often than others. In 18 of 80 years, the highest 24-hour rainfall was between 2 and 2.25 inches. In 15 years, the highest daily rainfall total was between 1.75 and 2.0 inches. Only one time in 80 years was there a daily record above 5 inches.
However, the other thing that is clear is that the spread is incomplete. In this example, there are no years in which the highest daily rainfall total was between 4 and 4.5 inches, but there are some cases between 4.75-5 inches and 5.25-5.5 inches. It’s not physically plausible that the atmosphere would just never produce those rain amounts. It’s more logical to assume that if we had enough data going far enough back or forward in time, that there would eventually be a daily event filling in the gaps.
This is where statistics come in. Scientists apply what they call a “distribution” (the dark line in the figure below), a relationship of the magnitude of the rainfall to how often that rainfall amounts occurs (2). The distribution line is like the final picture of the dinosaur. It uses the observations (bones) as the input for a reconstruction of the whole climate picture.
The observations from Beckley, WV, of the frequency of rain events of different sizes (dots inside bars) can be used to estimate the full range of likely events and their frequency (dark line). This statistical estimate is called the probability density function, and it's like the process of using the bones from an incomplete dinosaur skeleton to describe what the complete creature probably looked like. Graph by NOAA Climate.gov, based on data from NCEI.
And now, researchers can see how often an event of any rainfall amount is likely to occur. In fact, if we consider the total area under the curve (dark line) and recognize that it must equal 1.0 (100%), then the probability of a single event of a given size occurring at some point is simply the area under that portion of curve (dark line). The probability of a yearly daily maximum rainfall event greater than 4 inches, for example, is just the area from 4 on the x-axis to the right, bounded by the distribution line.
In this type of graph, the curved line marks a hypothetical list of all possible extreme rainfall events, with the caveat that the total area under the curved line must equal 1.0 or 100%. The percent chance of any single rain event being more than a specific amount is the percent of the total area to the right of that rainfall amount. The percent chance of a rain event less than or equal to that threshold can be found by subtracting the area to the right of the threshold from 100. Graph by NOAA Climate.gov.
Since we can figure out the probability for a given rainfall amount, we can also figure out what rainfall amounts correspond to specific probabilities like 0.1%, or said another way, a 1-in-1,000 year event (1/1000).