Don’t look now, but it’s winter outlook verification time! A time when I like to take readers on a statistical sojourn, a mathematical migration, through last year’s winter outlook. By the end of this post, you’ll have been provided with a non-alcoholic, numbered nightcap in the form of an answer to the question, “How did the winter outlook do?” (Sorry. I’m in an alliterative mood.)
But first, my yearly warning that some math is involved in this post. Because while it may be easiest to just use our eyes to verify whether the forecast map matched the real winter, our eyes can lie. Plus, we aren’t looking for an easy way out. We’re the ENSO Blog, and you are ENSO Blog readers, for Pete’s sake! No one here is afraid of math, right?! And to properly put this year’s outlook into perspective, we need to use consistent statistical metrics. The good news is that I’ve already covered the complicated aspects of statistical verification in eight previous posts on the ENSO Blog. One. Two. Three. Four. Five. Six. Seven. Eight. Head there to nerd out.
Still, I’ll repeat some basics: The winter outlooks for temperature and precipitation are for the probability that seasonal average temperatures and total precipitation will be either above average, below average, or near average. The Winter Outlook map we normally see is showing only the category that has the highest chance of the three. If all chances are the same (called “Equal Chances”), the location is colored in white (1). Note that the “highest” probabilities are never that high. In last winter’s outlook, the highest probability issued was 50-60%. There is simply too much uncertainty in seasonal predictions to warrant probabilities anywhere close to 100% in any of the three categories (2). It’s important to remind ourselves what these outlooks are actually predicting before we even begin to look into how they did.
Let’s verify! (3)
Did the temperature outlook run hot…or cold?
The winter temperature outlook for 2022-2023 favored warmer-than-average seasonal temperatures across the eastern and southern contiguous United States and cooler-than-average temperatures across the northern tier from the upper peninsula of Michigan to the West Coast.
This outlook bore a close resemblance to the previous two winter outlooks as well as the typical winter impacts during La Niña. All of this makes sense as this was the third straight Northern Hemisphere winter with La Niña conditions present. But, while the state of the El Niño-Southern Oscillation (ENSO) is one of the two best sources of skill in seasonal predictions (the trend is the other), it’s not the only influence.
In reality, winter temperatures across the contiguous U.S. exhibited a pretty dramatic east/west split, as the eastern half of the country observed above-average temperatures (top five warmest on record for 19 different states), while temperatures were below average across the western half of the country.
Overall, it was a pretty good temperature outlook. The warmth over the eastern and southeastern U.S. verified as well as the cooler-than-average temperatures for the northern tier from the Dakotas on west. Eyeballing this, the biggest differences between forecast and actual conditions were over the Southwest and the northwestern Great Lakes.
But don’t trust your eyes, VERIFY (with math). For this, the Climate Prediction Center uses a metric known as the Heidke Skill Score (HSS) (available here). The HSS is a tough scoring system. It doesn’t just grade the outlook based on how often it hit or missed the mark. It also subtracts some points based on how often the forecast might have been right simply by luck. An HSS score greater than zero means the outlook was better than randomly choosing one of the three categories (meaning the forecast had “skill”.) The better the forecast, the higher the HSS. (4)
And the HSS for the 2022-2023 winter temperature outlook was nearly 30 for the contiguous US, and nearly 39 when only looking at areas where forecasters favored one category over the others (non-equal chance areas). Meaning this outlook (like 8 of the last 10, and 16 of the last 20 winter temperature outlooks) was better than a random forecast.
Predicting the future is never going to be 100% on the mark. Our planet is simply too chaotic, our observations too imperfect, and our models too uncertain to expect every winter outlook to be “THE BEST EVA”. That’s why the outlooks use probabilities, and that’s why it is all about playing the “long game” with seasonal outlooks. After all, 16 out of 20 winters with HSS scores above 0 ain’t bad at all.
Did the precipitation outlook leave us high and dry?
The 2022-2023 winter precipitation outlook favored a drier-than-average season for the southern tier and wetter-than-average conditions for the northern tier and Midwest. Like the temperature outlook, the precipitation outlook also was similar to the outlooks during the past two winters due to the continued existence of La Niña.
Overall, the precipitation outlook had an HSS of 9 for the country as a whole, which rose to 12 when looking just at locations where the outlook favored one category over the others (i.e., leaving out the “equal chances” areas. Which is good! Again, that’s better than a random guess, and precipitation is generally harder to predict than temperature.
The outlook favoring a drier-than-average winter in Texas and Florida was right on, along with favoring a wetter-than-average winter for the Great Lakes and Northeast. But out west, the Pacific Northwest ended up drier than average, and southern California wetter than average. This is a good example of how the forecast as a whole can have skill even if the outlook favored the wrong category in a couple of regions.
So, what happened out West?
Nat did an excellent job at the blog showing how above-average precipitation in the Southwest can sometimes occur during a La Niña winter and how this year’s drenching rain and heavy snow might just have been the result of seasonally unpredictable weather conditions. I know that isn’t a satisfying answer. But when it comes to Earth, sometimes that’s the best we’ve got.
Why? Because sometimes, relatively small differences in expectation can lead to big differences in outcomes. Let me explain. When we talk about a “La Niña pattern” for winter, that means, averaged over the entire season, an area of higher pressure than normal south of Alaska over the North Pacific and a retracted jet stream across the Pacific. And that’s pretty much what happened, averaged across the whole winter. With one change. Let’s compare this year to the average of the four strongest La Niña winters since 1950 (1973-74,1975-76, 1988-89, and 1999-2000). In 2022-2023, the high-pressure anomaly was a bit farther west and north, which also meant the low-pressure anomaly was farther west and south, right over the West Coast (5).
Simply put, when it comes to the winter atmospheric pattern, this doesn’t seem to be a case where the models and forecasters were predicting a dog, and in reality was an ice cream cone. It was more like they were predicting an English bulldog, and the reality was a French bulldog. But, still, that difference was enough to see tremendous rain and snow across California that washed out roads and stranded residents in their houses for days.
Let’s not forget, though, that even with the miss in parts of the West, this was another pretty good outlook since the HSS scores were positive. That means that CPC forecasters were able to pull out some of the climate signals ahead of time. Which is quite amazing, given they are up against the relentless, uncertain chaos of our atmosphere.
Thank you to Johnna Infanti, Matt Rosencrans, and Scott Handel for their review and feedback on this post.
- Let’s break this down a bit more in case anyone is still confused. Using the winter temperature outlook map as an example, for any point on that map, there are actually three forecasts being made: one for above-average temperatures, one for below-average temperatures, and one for the chance of near-average temperatures. When added together, the probabilities will equal 100%.
- Why is there so much uncertainty? Here’s a brief list of other things besides ENSO that can impact seasonal patterns: climate trends, sudden stratospheric warmings, the Madden-Julian Oscillation (MJO), the Arctic Oscillation/North Atlantic Oscillation, the North Pacific Oscillation-West Pacific teleconnection, and the Pacific/North American pattern. And pretty much nearly all of these are not that predictable months in advance when the outlooks are made.
- Reminder that this post is about the verification of the updated Winter Outlook made in mid-November 2022, not the Winter Outlook issued in mid-October 2022.
- The HSS equation is HSS = 100*(H-E)/(T-E), where H is the number of correct forecasts, E is the expected number of correct forecasts by luck, and T is the total number of forecasts. A score of 100 means a perfect forecast, a score greater than zero means a forecast is better than expected from luck, and a score of -50 means the forecast was completely wrong with the favored category not occurring anywhere on the map.
- Extending this picture even farther to the west, a large low-pressure anomaly was also present over Siberia, a feature that was not present during any of the four strongest La Niñas. In 2023, you can clearly see a Rossby wave train spanning almost the entire planet: high, low, high, low high, low.
This post first appeared on the climate.gov ENSO Blog and was written by Tom Di Liberto.