2022-23 Hudson Valley winter outlook
So does a hot, dry summer mean that we’re going to “pay for it” this winter? How does a cool start to fall factor in? ❄️
It is that time of the year once again: just over a week til Halloween, 5 til Thanksgiving, 9 til Christmas, and 0 til you learn about what the upcoming winter might have in store
Since 2004, I’ve been slinging snow day predictions: from the halls of Valley Central, to the laboratories of SUNY Oswego, to the frenetic forecasting room at AccuWeather, to Middle-earth New Zealand.
I’ve now lived “down under” for almost seven years — 8,500 miles away from New York with a time difference of 18 hours, but distance has no bearing on my dedication.
As you’ve shown me, dedication is a two way street. Your unwavering support of my forecasting efforts is my main motivation to keep going. That’s why I’ve decided to offer something new this winter.
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Winter 2022-23 — what you need to know
A seasonal forecast is like 500 piece jigsaw puzzle. You start putting it together and notice that a number of the pieces are missing. You finish the puzzle, but there are some gaps. The picture is a bit fuzzy, but you can make out the theme.
It isn’t perfect.
Over the course of an entire season, weather will vary — there will be puzzle pieces of many different colors! But what types of weather (puzzle piece colors) are we most likely to see? That’s what a seasonal outlook tries to answer.
Every year, forecasters worldwide try their hand at long-range predictions in the hopes of finding the missing piece that others missed.
In New Zealand, it’s what I do for a living: assess long range weather and climate patterns to help people plan and prepare for things like floods, drought, hurricanes, and heatwaves. I’m in my element with this post.
Now, it’s time to give you what you came for:
5 things you should know about winter 2022-23 in the Hudson Valley 🌨️
I lean toward a winter that’s somewhat harsher than last year 🥶
An increased frequency of storm systems tracking in from the west during January-March with an increased frequency of disruptive winter weather 📈
However, harsh winter weather may not last too long and be quickly followed up by milder conditions
🔵 Mid and/or late November and December may have a period of colder than normal conditions followed by less frequent cold spells than normal during during mid and late winter 🔴
I’m not expecting the coldest of winters, but it will have its moments!
In terms of snow, November and December could go either way, January and March have the highest chances of being snowier than normal, and the odds for big February storms look a bit lower than normal ❄️
There’s a higher chance for mixed precipitation events (e.g., sleet, freezing rain) due to somewhat milder temperatures and westerly storm tracks ⛸️
Wildcard factor: very warm seas in the Atlantic Ocean could fuel stronger coastal snow storms (nor’easters) — one blizzard could change the complexion of the season! ☃️
The Hudson Valley’s first inch of snow typically occurs sometime between November 23rd and December 16th with the median date being December 4th.
That’s 32 to 55 days from today, October 22nd. The clock is ticking ⏳ 😉
Snow day robot
🤖 I’ve got another fun way of tracking the potential for snow days this winter: my Hudson Valley “Will it snow?” Twitter bot is live at https://twitter.com/Will_It_Snow_HV
Three times a day, it scans the weather forecast over the next week to see if the magical four letter word is mentioned… ❄️
Satisfied with the executive summary? Then you’re done here! Want to watch a video of me delivering the outlook? Click here.
However, if you’re a weather nerd, keep reading and follow me into the meteorological mountains…
Winter 2022-23 — a deep dive
Winter used to be worse
👴 “Back in my day, we walked 5 miles, uphill, and through a foot of snow to get to school!”
Have you ever had that classic conversation with grandma or grandpa?
It naturally begs the question: were winters of old worse? Did grandma and grandpa deal with harsher winters and do they have a point when they say we might be a bit “soft” now? Well what do you know, the data says yes!
I took winter temperature, wind, and snowfall data for the Hudson Valley area, created a common scale, and then combined them to form something akin to an unofficial “winter severity index”.
You can see the results of this exercise for every winter from 1958 up until last winter below. Blue boxes indicate more severe winters and red boxes milder ones. The years are ranked from worst (#1) to least bad (#64).
Clearly, there are more red boxes toward the bottom of the chart, suggesting that modern winters just aren’t as bad. That’s a function of warmer average temperatures, less frequent extreme cold, and a slight trend toward less snow. It doesn’t mean that a “really bad” winter isn’t possible in the year 2022-23, but you probably wouldn’t want to bet on it.
The 2014-15 winter is the most recent example of a “winter of old”, when in February 2015 the dreaded polar vortex descended on the U.S. and brought bone-chilling, sub-zero temperatures for weeks on end.
Climate models produce the same temperature, wind, and snowfall data, but in a predictive form.
I applied the same formula to rank the 2022-23 winter… and… drum roll please! 🥁🥁🥁
It ranked 36th, putting it on par with winter 2020-21 (two winters ago) and making it somewhat harsher than last winter.
By no means is this an exhaustive analysis, but it does provide a glimpse into how harsh the upcoming winter may be, relative to ones of the recent past.
❄️ First snow - when does it occur, on average?
Learning from the past to prepare for the future. It’s one of the overarching themes of this post! I wanted to answer the simple question: when does the first inch of snow typically fall?
💡 Answer: Over the last 10 years, the first inch of snow occurred during the first week of December on average (December 5th). The first inch happened as early as November 7th (in 2012) to as late as January 7th (in 2022).
(☝️ perhaps this tidbit will be useful for those competing in a “predict the first snow day” contest 😅)
In the last decade, November featured the first snowfall on 4 occasions, December 5, and January 1.
The first inch of snow during the last two winters was the 2nd latest and latest in the last 10 years.
I hear the math teachers saying “but Ben, 10 years isn’t a big enough sample size!” 🤓 Agreed! So let’s look at the full distribution (as full as I could find).
This box-and-whisker plot shows the distribution of first snowfall data at Poughkeepsie over 70+ years, starting in 1931.
The middle line in the blue box represents the median first inch of snow date: December 4th, basically mirroring the analysis of the last 10 years.
The lower quartile (25th percentile) is November 23rd — that means about 75% of the time, the first snowfall happens after November 23rd.
The upper quartile (75th percentile) is December 16th, meaning that about 25% of the time does the first inch of snow fall after December 16th.
This helps to contextualize how unusual last winter was, when the first inch didn’t fall until January 7th. Only 3 winters had a later first inch of snow: 1999 (January 9th), 2000 (January 14th), and 1955 (January 22nd).
Take home message? The Hudson Valley’s first inch of snow typically occurs sometime between November 23rd and December 16th with the median date being December 4th.
That’s 32 to 55 days from today, October 22nd. The clock is ticking ⏳ 😉
🔮 Analog years - learning from the past
Past experiences help us prepare for the future in all aspects of life, not just weather and climate!
Using teleconnections, which are described a little later on, I sift through past years that had similar climate characteristics to the present — these are called analog years.
There’s no such thing as an exact match, so an average of several analog years tends to be more powerful. By blending the years together, it can be determined if a month or season is more likely to be mild, cold, rainy, snowy, or dry.
In a nutshell, if several analog years have a similar flavor, forecast confidence increases.
My analog years, along with the percentage of normal monthly snowfall that fell in the Hudson Valley during each, are listed in the table below. “100%” means exactly normal snowfall for the month. Anything over 100% indicates above normal and less than 100% indicates below normal.
There are a couple of key takeaways:
Snowfall during November and December analog years was a mixed bag: 2 of 8 years had above normal (>100%) November snowfall and 4 of 8 had above normal December snowfall
The average of the analog years yields 130% of normal snowfall during January — 6 out of 8 years had above normal snowfall
6 out of 8 analog years had below normal snowfall during February
The average of the analog years yields 105% of normal snowfall during March
2 out of 8 analog years had notable April snow
If I was to build a snowfall risk profile for the season ahead, it would favor January as having the potential to be snowiest with a reduction in the odds for big February storms. I’d describe November-December as “could go either way” with December in particular showing some potential. The snow signal for March leans above normal.
💽 Climate models - providing winter weather whispers
Some people look forward to Thanksgiving, Christmas, or a birthday, but for climate scientists, the 14th of every month is a holiday 🎁
Since 2019, it has marked the day on which a coordinated “data dump” from the world’s top meteorological centers occurs.
This data provides insights on what the weather might be like — not over the next few days, but over the next six months.
It’s up to meteorologists to tell its story 📖
At 5:00 am on the 14th of October, my computer roared to life. Not because I was rising early, but because a “cron job” was triggered, an internal reminder that’s used to instruct a computer to perform a task at a certain time — in other words, a computer’s alarm clock.
By 5:05 am, my computer’s fan was audible, as it was feverishly sifting through freshly downloaded data from the United Kingdom, Germany, Italy, France, Canada, the U.S., and Japan, beamed to my little laptop at the bottom of the Earth in New Zealand.
My Python scripts were busy turning the data, encoded in a special meteorological file type called “.grib”, into pictures that you or I can understand.
A picture in this case is the last link in a long chain that can start with something as simple as a sea temperature observation from a boat in New York Harbor.
Thousands of observations are used to construct a snapshot of the current state of the climate before sophisticated mathematical computer models run simulations of the future climate.
Each simulation is called an “ensemble member”. Each ensemble member is like an opinion. When the members have a similar opinion of the future state of the climate, forecast confidence is higher.
The “opinion” of each of the international centers is combined to create a super ensemble. Individually, the ensemble members make noise, but together, they make music.
The wisdom of the crowd is almost always more powerful than the wisdom of one.
By 5:20 am, colorful images were populating in different folders, one called temperature, another called snow, and several others with names that only weather nerds would understand.
The output of these climate models is typically assessed as an anomaly or a difference from average. It allows someone to clearly see what parts of the world are most likely to be warmer 🔴 or colder 🔵 than average and wetter 🟢 or drier 🟤 than normal.
Useful predictions are possible out to about 3 to 6 months, but skill declines as the influence of the initial oceanic and atmospheric conditions that are fed into the model wane.
At any rate, it’s time to do a little crystal ball gazing 🔮
Blobs of red and blue, what do they mean? Lava from a volcanic eruption 🌋 meeting the icy abyss of the polar bears’ 🐻❄️ north? Not quite.
The long-range forecasting funnel starts wide. We want to answer the question: what does the broad circulation pattern look like? In other words, where is high and low pressure forecast to be?
The map below takes us to 500 hectopascals, some 20,000 feet above Earth’s surface. Things here, above even the tallest mountains in the continental USA, are a little less chaotic than at the ground. That means the models tend to be a little more accurate.
Red colors indicate higher atmospheric heights, which generally imply more settled and milder weather. Blue shades indicate lower atmospheric heights and signal disturbed, colder conditions.
Opinions from 8 climate models are shown. All of them signal a high pressure ridge (🔴) somewhere near the U.S. East Coast, which generally is not an ingredient in the recipe for a wall-to-wall harsh winter.
Several models hint at frequently disturbed weather (🔵) in the northern tier of states.
This would imply that the Hudson Valley could be located near a battleground of warmer, drier air to the south/east, and cold, stormier conditions to the north/west.
Having a ridge (🔴) in the south and near the East Coast isn’t particularly ideal for those coastal snowstorms (nor’easters) that dump lots of snow. It doesn’t mean that they can’t happen, but perhaps the ingredients to bake that cake come together less frequently 🧑🍳
Problem is, we don’t live 20,000 feet above Earth’s surface 🙃
So we also look at…
The weather maps are always red these days. This isn’t by design. It’s Mother Nature’s way of saying she has a fever 🤒
The average temperature is rising, which means that there will be fewer cold extremes with time. This doesn’t mean that it won’t be cold -winter will always be winter- but perhaps those frigid periods are fewer and farther between and don’t last as long.
Although the map above suggests above average winter temperatures are most likely, the guidance for this winter is colder than what was issued this time last year for last winter.
Having somewhat milder temperatures doesn’t mean that it can’t snow, but it may mean that snow is more likely to mix with sleet, freezing rain, and rain, like we saw last winter ⛸️
The temperature doesn’t matter so much if it’s dry! If a ridge of high pressure rests near the East Coast more frequently than usual, it can block moisture from streaming from the Gulf of Mexico to the Northeast. In the map below, this is expressed by strands of brown (🟤 below normal precipitation) extending from Florida toward the Mid-Atlantic.
🟢 Shades of green stretch from the Ohio Valley into western New York, which may be indicative of a more westerly storm track.
These systems, called inland runners, tend to result in more mixed precipitation events for the Hudson Valley. Of the winter months, January and February are shown to have the most precipitation compared to normal in the Hudson Valley while December is signaled to be driest.
See the blob of dark green off the New England coast? That’s a sign of the model responding to unusually warm sea temperatures and producing a lot of precipitation.
The oceans off the East Coast are forecast to be several degrees warmer than average this winter. Warm seas are fuel for storms. I would describe this as a wildcard factor. One big storm can completely change the complexion of a season! So perhaps our “boom” risk for a powerful blizzard is higher than normal, but all the ingredients need to come into the mixing bowl first — which, if the models are correct, could prove to be a challenge.
Climate models also predict seasonal snowfall. Unlike air pressure, temperature, and precipitation, this isn’t a field that is routinely assessed by climate scientists.
I’ve been tracking its performance over the last few winters. It’s predicted lower than normal snowfall the last 3 winters and was accurate in 2 of the 3.
This winter, it puts the Hudson Valley on a knife’s edge: more snow to the north, less snow to the south. I think it’s suggestive of frequent storms tracking toward the region from the west and/or north and an increased amount of storminess in the January-March period in particular. Westerly storms tend to drag in warmer air, which is the reason why I favor more mixed precipitation events this winter.
Notably, the October outlook trended snowier compared to the one issued in September, especially for northern New York and New England.
Models are one tool in the toolbox 🧰 — as you read earlier, there’s analogs (past years with similar climatic conditions to the present) as well as an assessment of climate drivers (i.e., what has its hands on the steering wheel of Mother Nature’s car).
📞 Teleconnections - Mother Nature’s smart phone
Teleconnections are climate patterns related to one another over long distances. Think of it like a weather pattern in one part of the world texting and making plans with another in a different, distant part.
This includes things like El Niño and La Niña, which you may have heard of before, and other climate indices such as the Indian Ocean Dipole, North Atlantic Oscillation, Arctic Oscillation, Pacific Decadel Oscillation, Quasi-Biennial Oscillation, Southern Annular Mode, and more!
(quiz on Monday 🙃)
Climate forecasters seek to identify which of these climate drivers will influence the prevailing weather patterns in the season(s) to come. Put another way, the goal is to try to figure out what has its hands on the steering wheel of Mother Nature’s car.
There are no 1:1 relationships in climate forecasting: as much we crave simplicity, La Niña, as you’ll learn about below, doesn’t always bring a certain type of weather to the Northeast and Hudson Valley.
Lastly, it isn’t possible to predict the weather during Christmas week months in advance, but it is possible to gain a handle on the most likely types of weather over the coming months in a general sense. Think: milder, colder, rainier, snowier…
Did you know? 💡
When it comes to climate prediction, the starting place is the sea. Did you know that the oceans have a “memory”? Ocean memory, or the persistence of ocean conditions, is a major source of predictability in the climate system beyond short-term weather time scales.
The map below shows sea surface temperature anomalies (difference from normal) averaged over the last month. Red (blue) colors indicate warmer (cooler) than average conditions. I’ve added some colored-coded boxes for the key teleconnections for this winter.
La Niña 🟦 — a pattern of cooler seas in the equatorial Pacific that changes the jet stream patterns over the Pacific; typically, this results in a “dip” of polar jet stream in that flows into the northern U.S.
This jet stream dip can lead to more frequent intrusions of cold air in the Northwest, Rockies, northern Plains, and sometimes the Midwest and Northeast
La Niña usually results in a less active sub-tropical jet stream; this means less moisture and a tendency toward less snow for the Mid-Atlantic states
La Niña winters in the Northeast tend to be colder earlier, such as in December, and milder later, such as in February
Atlantic marine heatwave 🟥 — much warmer than average seas in the western and northern Atlantic Ocean can be extra fuel for coastal storms, a wildcard factor for the winter outlook this year
Warm North Pacific 🟧 — this blob of warm seas near the West Coast of North America has historically been associated with big high pressure systems. What goes up must come down: if there’s a warmer high in the west, there will be a colder low to match in the east
I think this ocean area will trend cooler as winter approaches, in association with the opposite pattern at times (warmer, drier east, cooler, stormier west)
Negative Indian Ocean Dipole 🟨 — marked by warm seas to the northwest of Australia, this driver can work in tandem with La Niña early in winter
Another, most unusual, teleconnection that we’ll have this winter relates to a volcanic eruption that occurred in the South Pacific Ocean in January.
You can read more about it in my premium post “The butterfly effect of a volcano”, but to cut a long story short, the huge amount of water vapor that was injected into the stratosphere following the eruption could work against the signal for reduced snowfall and lift the chance for winter storms.
Congratulations, 3,600 words later and you’ve made it to the end! I hope you found it to be an interesting and informative read.
As the leader of a project tasked with issuing long-range climate outlooks for New Zealand, it’s my job to bridge the gap between day-to-day weather, week-to-week trends, and seasonal themes in an easy-to-understand way.
Just a half-century ago, long-range prediction was the relative guesswork of climate soothsayers. Now, rich climate datasets generated on supercomputers around the world underpin reasonably good projections of the weather for the months to come.
Long range predictions aren’t perfect and likely never will be; however, many sectors contract services for long range outlooks to help with planning, decision making, and strategy. This could involve a department store having a larger or smaller stock of snowblowers based on climate expectations, the strategic placement of goods, like rock salt, in regions that are expecting a harsher winter season, or a farmer preparing him or herself for a drought.
I can only dream of where the science will be in century from now. My hope is that a climate scientist from the year 2122 stumbles upon this post and laughs about how “primitive” it is.
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