Hythergraphs

A couple of days ago, Amanda Young of UAF's Toolik Field Station posted a link to a new tool for visualizing climate data since 1988 at Toolik:

https://www.uaf.edu/toolik/edc/monitoring/abiotic/climate_summaries.php

The temperature data is incomplete prior to 1994, and unfortunately precipitation coverage is rather poor until the last few years, but nevertheless the presentation is worthwhile.  It's interesting to note that 2021 was the coldest year since 1999 at Toolik; this is a more significant cold anomaly than I had realized.  Both NOAA/NCEI and ERA5 indicate that 2021's average temperature fell at approximately the 25th percentile compared to the 1991-2020 climate on the north side of the Brooks Range, i.e. not as cold relative to "normal".

The Toolik website also presents hythergraph climate summaries.  This is a visual presentation that I hadn't come across before, although it seems it's been around for more than a century.  I decided to try my hand at the hythergraph, so here's one to illustrate the statewide monthly climate averages, per NOAA/NCEI data:

The chart has several attractive features.  It nicely summarizes the similarity or contrast between different months, with the "vector difference" on the chart showing the difference in this two-dimensional temperature/precipitation phase space.  For example, November is a lot more similar to December than it is to October, on a statewide basis.  The hythergraph also provides a nice synthesis of the combined annual cycles in temperature and precipitation, with the second half of the year being much wetter.

The comparison between the 1951-2010 and 2011-2022 trajectories also provides a useful quick look at how the last 12 years have compared to the earlier climate.  The striking difference has been the increased precipitation in August and September.  It's also notable that winters have been warmer recently.

A possible improvement on the diagram would be to include the typical year-to-year variability in each month, perhaps as a background shading.  This would reveal how significant the recent changes are compared to natural variability.

[Update December 4: Chris Swingley experimented with this idea, see his blog post here: https://swingley.dev/blog/p/2022/]

Here's one for Fairbanks:

The interesting shape is driven by the heavy concentration of precipitation in the warm season, skewing the top of the diagram to the right.  June through September have been notably wet in the last decade or so, but so have all of the cold season months except (presumably by chance) January.

Finally, Utqiaġvik, where the climate has changed so dramatically in the last 20 years or so.

The differences in September, October, and November are very striking; notice that October and November have become as wet as September and October used to be, respectively.  The much warmer and wetter climate of autumn and early winter is a response to much reduced sea ice, with open water providing not only a warming influence but also a lot of extra moisture.  In fact, every month of the year has been both warmer and wetter in the last 20 years.

I'm sure readers will observe other interesting aspects of this visualization; feel free to leave a comment.

AI Weather Forecasts

I'm sure many readers have come across recent headlines about new breakthroughs in Artificial Intelligence for weather forecasting; here's one example:

https://arstechnica.com/science/2023/11/ai-outperforms-conventional-weather-forecasting-for-the-first-time-google-study/

It's not always easy to distinguish between hype and reality when it comes to claims of new technological advances.  Is the enthusiasm justified in this case?  Are weather forecasts about to see a revolutionary step forward in accuracy?  I'll offer a few words of my own perspective, as someone in the "weather business".

First, there's no doubt that AI and Machine Learning (closely related concepts) have made amazing strides for weather prediction in the past few years.  In early 2021, ECMWF (the world's leading "traditional" weather forecasting organization) published a "road map" for the future of AI weather forecasts, anticipating a rather gradual pace of innovation and change; but it turned out that several leading technology companies achieved remarkable success in just the next 2 years.  Crucially, the latest AI models were suddenly revealed as being able to match or even beat the ECMWF's forecast accuracy according to some metrics.  ECMWF wrote about it here:

https://www.ecmwf.int/en/about/media-centre/science-blog/2023/rise-machine-learning-weather-forecasting

The headline result - exceeding the ECMWF's basic skill level - is a big deal.  It means these new models are legitimate competitors, and it's truly remarkable that the AI scientists have achieved this so quickly, with relatively early-stage, experimental models.  In contrast, the accuracy of traditional forecast models has developed steadily but very slowly over the years, relying on bigger and faster computers, more satellite data, and incrementally better physics in the models.  Modern weather forecast accuracy is a great scientific accomplishment; but now without warning it is being equaled (by some metrics) with an immature technology that presumably has a lot of room to improve.

Here's an example figure from ECMWF showing gradual improvement of skill for predicting Northern Hemisphere 500mb height at various lead times.  After all this work, the idea that an upstart new technology can suddenly jump in with similar or better skill is quite surprising and perhaps difficult to swallow!

But notice that I alluded to specific metrics that show the new models in a favorable light, and this is because the first generation of AI models has significant limitations.  For example, it seems the models don't do particularly well with extreme events, because they are guided by (constrained by) the historical data rather than the laws of physics.  It seems to me that models trained purely on historical data will always struggle in this way, but hybrid statistical-dynamical models are an obvious extension that would be more likely to handle unprecedented events.

Another area where the new models aren't yet fully capable is in terms of handling uncertainty.  Forecast centers like ECMWF have become adept at running traditional models in a way that encapsulates and predicts uncertainty, and this is extremely important for valuable real-world forecasting: we need to know the plausible range of possible outcomes.  In contrast, the first-generation AI models just give a single answer to the question: "given today's weather, what will the weather be N days from now?"

It's also worth noting that - at least for now - the new AI models rely on the traditional models to provide the initial conditions for the forecast, i.e. to specify what "today's weather" is in great detail around the globe.  This "initialization" process is itself a tremendous scientific achievement, requiring a very advanced model to "assimilate" data from the entire observing system and create a single best guess of what's happening at every location.

What Difference Does It Make?

It's worth asking what practical difference there will be for weather forecasts created with AI models rather than traditional models.  This is of course difficult to foresee, but I suggest the answer is "not much" until the mainstream weather industry fully comes to grip with the new technology and builds it into the forecasting process.  The AI models have been developed by big technology companies (e.g. Google and NVIDIA) that are not (yet) in the business of selling forecasts, and the vast majority of meteorologists and meteorological scientists work elsewhere in government, academia, and traditional private sector weather companies.  These two "worlds" will need to come together if the new technology is to be deployed widely, and the process won't be quick or painless.

In terms of specific predictions, I can see two things happening.  First, the big-tech AI models will be licensed to private weather companies who find value in the AI forecasts and can build products for customers.  The leading forecast centers like ECMWF will also build their own AI models and provide the results to users, and so the industry will gradually adapt to the new source of information; but the traditional methods certainly won't be ditched any time soon.

Second, I think the AI methods will be used to develop much better forecasts for some specific high-impact problems that are not handled well by traditional models.  An example might be fire weather forecasts: for example, an AI model trained on past weather-driven fire events could provide powerful guidance for future risk.  The deadly fires in Hawaii and California in recent years might have been much better predicted with a specific application of the new technology, allowing aggressive early evacuations in those rare and dangerous weather situations.

I'd be glad to hear comments and insights from readers.  What else do AI methods have to offer that physics-driven "deterministic" models can't provide?  What other forecast problems are poorly handled by today's usual weather guidance and might be particularly amenable to historical/statistical methods like AI?

Wild Weather in the South

Weather is never very far from the news in a place as big as Alaska, and the more extreme events aren't usually good news.  Yesterday a major Gulf of Alaska storm barreled into a cold air mass and high pressure over southern Alaska, and the results were dramatic: damaging winds in the Matanuska Valley, and a major landslide down south in Wrangell.  Sadly the landslide brought loss of life, and perhaps worse than the 2020 Haines landslide.

ADN news links:

https://www.adn.com/alaska-news/mat-su/2023/11/21/most-power-restored-and-roads-cleared-following-mat-su-windstorm/

https://www.adn.com/alaska-news/2023/11/21/large-landslide-south-of-wrangell-closes-highway-draws-search-and-rescue-team/

The rain in the Southeast doesn't appear to have been particularly unusual for the region; the map below shows 72-hour totals (click to enlarge).  But it was pretty wet earlier in the autumn, so there may have been a compounding risk.  Sitka is in the top 10 for rainfall since September 1.

As for the winds around Palmer and Wasilla, the map below shows how localized the wind storm was in the greater Anchorage area (showing yesterday's peak gusts in mph).

This looks like a great example of channeled downsloping flow from the east, driven by a strong pressure gradient.  The map below shows the sea-level pressure analysis at 3pm yesterday, courtesy of Environment Canada.  Check out that pressure gradient over the southeastern coast of Alaska; high winds were seen throughout the region.

A Few Notes

A few comments on various topics.  First, the snow onslaught in Anchorage: the recent 10-day total of 38" amounts to the 3rd highest on record.  Last year's December snow was even a bit greater, and the top event was back in February 1996.

It's really remarkable to see such a large amount of snow at the airport - where there is typically less snow than across most of the area - and in back-to-back years.  With La Niña last winter and El Niño this winter, we can't pin the blame on similar climate forcing, so perhaps it's just "luck of the draw".

Second, the Tanana River at Nenana froze up last week on Wednesday the 8th.  The scene is suitably wintry today:

But the mighty Yukon River hasn't frozen over at Dawson yet; here's the latest video from the webcam:

The situation at Dawson looks about the same as last year at this time:

https://ak-wx.blogspot.com/2022/11/historical-context-for-warmth-freeze-up.html

Long-time readers will recall discussion of the lack of a proper freeze-up all winter long at Dawson in some recent years:

https://ak-wx.blogspot.com/2020/02/yukon-frozen-at-dawson.html

This autumn has certainly been on the warm side of normal in Dawson, but it's not breaking records for lack of freezing potential: here's a chart of accumulated freezing degree days (accumulated temperature differential below 32°F):

This is from the reliably-reporting "LRP" site in Dawson, not the airport, where many observations are missing.  The city does have a much longer history of climate observations, of course, but it seems there are some discrepancies between the different climate sites that would affect any long-term trend analysis.

And finally, I'll highlight the remarkable lack of sea ice along Alaska's west coast at this time, related to persistent southerly winds and warmth.  Rick Thoman posted a graphic to illustrate:

"Sea ice concentration analysis for Wednesday and the same date last year from the National Weather Service Alaska Region Sea Ice Program. Much less ice in the southern Chukchi and Bering Seas currently than this date last year. November 15 and effectively no ice in Kotzebue Sound is especially shocking, even by recent norms."

https://mastodon.social/@AlaskaWx@alaskan.social/111417939896937766

Here's a longer archive of November 15 analyses, from 2021 (top) to 2018 (bottom):

South Coast Snowstorm

Yesterday a number of 747 cargo planes were spotted at Fairbanks airport, and that usually means only one thing: Anchorage is having a big snow event.  And indeed they were: 2-day snowfall amounts ranged from 17" at the airport to 30" on the Anchorage Hillside and over 36" up Turnagain Arm.  Here's a graphic courtesy of NWS Anchorage, showing storm totals as of this morning:

With substantial snow earlier in the week as well as more today, the weekly total is really something: Brian Brettschneider indicates that the airport's 6-day total of over 28" is the third highest on record in the Anchorage climate history.  The only larger snow events in that time frame over the last century or so were in February 1996 and December 1955.  Quite a way to start off the winter!

The snow depth of 21" yesterday afternoon is easily the greatest for this early in the season in Anchorage.

Of course, far more snow fell in topographically favored locations.  Courtesy of NWS Anchorage (and publicized by Brian), here's an astonishing report from the Richardson Highway:

The liquid-equivalent precipitation estimates from the NWS are wild too, with over 10" estimated in the high terrain on Wednesday, and another few inches yesterday.

Here's a satellite loop showing the powerful cyclone and associated "atmospheric river" that brought such prodigious moisture to the south coast.

Yesterday morning's 3am surface analysis shows the 964mb low tracking directly into the Kenai Peninsula and Anchorage area.  Winds gusted as high as 60mph in Yakutat.

And here's a nice loop of the Anchorage-area radar reflectivity, courtesy of Randy Chase on Twitter:

https://twitter.com/DopplerChase/status/1723058354732282352

October Climate Data

Rick Thoman has a nice write-up of October's Arctic and Alaska climate anomalies here:

https://alaskaclimate.substack.com/p/october-2023-climate-summary

The precipitation contrasts across Alaska were striking: it was much drier than normal for the time of year in the southwest and south-central, but the North Slope and eastern Alaska were notably damp.  Rick notes that the Utqiaġvik CRN site measured 1.68" of liquid-equivalent for the month - the highest on record there for October (at either the CRN site or the much longer Barrow/Utqiaġvik climate site).  It's also the fourth consecutive month with more than an inch of precipitation at the CRN location.

Snowfall was also relatively abundant in eastern Alaska: as high as 19" in Tok and 17" in Eagle.  Also 18" on Keystone Ridge outside of Fairbanks.

Here's a look at snow water on the ground at 5-day intervals through the month, expressed in terms of historical percentile, and estimated by the ERA5 model:

The snowpack was established on October 6 in Fairbanks (considerably earlier than normal), but very little snow remained by the end of the month in the southwestern quadrant of the state.  The last week of the month was very mild, but chilly weather earlier in the month allowed for a near-normal monthly average temperature for much of the interior.  The Aleutians, North Slope, and Southeast were notably warmer than normal, however.

As for wind, October was relatively calm for the southwest and south-central, but windier than normal in the north and northeast.

Here's the mid-atmosphere pressure pattern that contributed to the surface climate anomalies: the ridge over southwestern Alaska explains the dry and calm weather there.

Sea surface temperatures remain much higher than normal in the western Bering Sea and northwestern North Pacific, so we can expect that winds from that direction will tend to bring unusually mild conditions to Alaska in the coming months.

Given that we have a strong El Niño in play this winter, which tends to produce a strong Aleutian low and frequent southwesterly flow across Alaska, I'd say the chances of a very mild winter are much higher than normal.  NOAA's Climate Prediction Center agrees:

For a comprehensive discussion of the winter outlook, check out Rick Thoman's November 17 presentation here:

https://uaf-accap.org/event/nov-climate-outlook/

Turning Warm

After relatively cool conditions for much of autumn in Alaska as a whole, the last week of October brought a change to unusual warmth.  Blame a big ridge extending north from the Gulf of Alaska, allowing warm air to be transported northward into the western part of the state and up to the Arctic.  Here are the average 500mb height and 850mb winds for the week ending October 30:

And the resulting 850mb temperature departure from normal:

Warmth in Alaska's far north has been an oft-repeated theme in the past two decades, and this year Utqiaġvik ended up with a top-5 warm October, although it wasn't as extreme as 2016 and 2019.  Here's the now (I trust) well-known chart of October temperatures there, showing the profound change in the local climate from loss of October sea ice.

For the state as a whole, October 30th was the warmest day of the month in comparison to normal.  The figure below shows the widespread distribution of unusual warmth, with the statewide average climbing into the 95th percentile for the time of year.  The standardized anomaly numbers reflect my calculations that account for seasonal skewness, as discussed recently here.