climate change

Minnesota ICE-OUT update

/ Paul Pukite (@whut) / 1 Comment

This is an update to analyzing the dates of Minnesota lakes ice-out events, as described on this blog years ago: Ice Out

The trend has been that dates have been creeping earlier, corresponding to warmer winters.

Scatter plot showing the relationship between the year and the number of ice-out days in Minnesota at latitude 43 N, with a fitted trend line indicating a slight decreasing slope.
Scatter plot showing ice-out days in Minnesota at latitude 44 N over the years from 1840 to 2040, with a trend line indicating a slight decline.
Scatter plot showing the trend of ice out days per year in Minnesota (Latitude: 45 N) from 1860 to 2020. The fitted line indicates a slight downward trend, with the y-axis representing ice out days and the x-axis representing years.
Scatter plot showing ice out days per year from 1880 to 2023 in Minnesota, with a fitted regression line indicating a slight downward trend.
Scatter plot showing the trend of ice out day data over the years in Minnesota at latitude 47 N, with a fitted slope indicating a decrease in days per year, represented by red circles and a blue trend line.
Scatter plot showing the relationship between year and ice-out day for Minnesota at latitude 48 N, with fitted regression line indicating a negative slope.

This is all automated, pulled from JSON data residing on a Minnesota DNR server. I hadn’t looked at it for a while, as the original client query assumed that the JSON was in strict order, but the response changed to random and only recently have I updated. The same approach used is to access lake data from common latitudes and do a least-squares regression on each set. The software is described here and available here, based on a larger AI project described here.

User interface displaying a form for plotting data from 1843 to 2025 with a specified latitude of 44.0, accompanied by options to clear data and submit the request.

There are seven anomalous data points1 that point to ice-out dates prior to January, which may in fact be faulty data, but are kept in place because they won’t change the slopes too much

Summary

2013 slopes2026 slopes
43 N-0.066-0.1042
44 N-0.047-0.08595
45 N-0.068-0.10873
46 N-0.0377-0.0416
47 N-0.0943-0.04138
48 N-0.1995-0.0835

The overall average is around -0.08 days earlier per year which amounts to 8 days earlier ice-out over 100 years.

The last “year without a winter” in Minnesota was 1877-1878 which corresponded to a huge global El Nino. One can perhaps see this in the 44 N and 45 N plots showing early outliers but the data was sparse back then. More obvious is the short winter of 2023-2024, where many lakes never froze or one close to my place was really only solid for a time in December. Can look up news stories on this such as the following 

2024: The Brainerd Jaycees Ice Fishing Extravaganza on Gull Lake—one of the world's largest—was canceled for the first time in its 34-year history because the ice was too thin to support the event.

Footnotes

  1. The following lakes showed anomalous ice-out dates, assumed to be late in the previous year or late in the current year, the latter which would be physically impossible
    Lake Cotton @ 46.88259 N (11/23/2010 [day -39.0]))
    Lake Leek (Trowbridge) @ 46.68309 N (12/07/2021 [day -25.0]))
    Lake Little Wabana @ 47.40002 N (12/08/2021 [day -24.0]))
    Lake Star @ 45.06337 N (11/20/2022 [day -42.0]))
    Lake Lewis @ 45.7479 N (11/28/2022 [day -34.0]))
    Lake Unnamed @ 44.81299 N (11/25/2023 [day -37.0]))
    Lake Unnamed @ 44.81299 N (11/26/2024 [day -36.0])) ↩︎

Topology shapes climate dynamics

/ Paul Pukite (@whut) / 3 Comments

A paper from last week with high press visibility that makes claims to climate1 applicability is titled: Topology shapes dynamics of higher-order networks

The higher-order Topological Kuramoto dynamics, defined in Eq. (1), entails one linear transformation of the signal induced by a boundary operator, a non-linear transformation due to the application of the sine function, concatenated by another linear transformation induced by another boundary operator. These dynamical transformations are also at the basis of simplicial neural architectures, especially when weighted boundary matrices are adopted.

\dot{\theta}_i = \omega_i + \sum_{j} K_{ij} \sin(\theta_j - \theta_i) + F(t)

This may be a significant unifying model as it could resolve the mystery of why neural nets can fit fluid dynamic behaviors effectively without deeper understanding. In concise terms, a weighted sine function acts as a nonlinear mixing term in a NN and serves as the non-linear transformation in the Kuramoto model2.

Continue reading →

20yrs of blogging in hindsight

/ Paul Pukite (@whut) / Leave a comment

Reminded by a 20-year anniversary post at RealClimate.org, that I’ve been blogging for 20 years + 6 months on topics of fossil fuel depletion + climate change. The starting point was at a BlogSpot blog I created in May 2004, where the first post set the stage:

Click on the above to go to the complete archives (almost daily posts) until I transitioned to WordPress and what became the present blog. After 2011, my blogging pace slowed down considerably as I started to write in more in more technical terms. Eventually the most interesting and novel posts were filtered down to a set that would eventually become the contents of Mathematical Geoenergy : Discovery, Depletion, and Renewal, published in late 2018/early 2019 by Wiley with an AGU imprint.

The arc that my BlogSpot/WordPress blogging activity followed occupies somewhat of a mirror universe to that of RealClimate. I initially started out with an oil depletion focus and by incrementally understanding the massive inertia that our FF-dependent society had developed, it placed the climate science aspect into a different perspective and context. After realizing that CO2 did not like to sequester, it became obvious that not much could be done to mitigate the impact of gradually increasing GHG levels, and that it would evolve into a slow-moving train wreck. That’s part of the reason why I focused more on research into natural climate variability. In contrast, RealClimate (and all the other climate blogs) continued to concentrate on man-made climate change. At this point, my climate fluid dynamics understanding is at some alternate reality level, see the last post, still very interesting but lacking any critical acceptance (no debunking either, which keeps it alive and potentially valid).

The oil depletion aspect more-or-less spun off into the PeakOilBarrel.com blog [*] maintained by my co-author Dennis Coyne. That’s like watching a slow-moving train wreck as well, but Dennis does an excellent job of keeping the suspense up with all the details in the technical modeling. Most of the predictions regarding peak oil that we published in 2018 are panning out.

As a parting thought, the RealClimate hindsight post touched on how AI will impact information flow going forward. Having worked on AI knowledgebases for environmental modeling during the LLM-precursor stage circa 2010-2013, I can attest that it will only get better. At the time, we were under the impression that knowledge used for modeling should be semantically correct and unambiguous (with potentially a formal representation and organization, see figure below), and so developed approaches for that here and here (long report form).

As it turned out, lack of correctness is just a stage, and AI users/customers are satisfied to get close-enough for many tasks. Eventually, the LLM robots will gradually clean up the sources of knowledge and converge more to semantic correctness. Same will happen with climate models as machine learning by the big guns at Google, NVIDIA, and Huawei will eventually discover what we have found in this blog over the course of 20+ years.

Note:
[*] In some ways the PeakOilBarrel.com blog is a continuation of the shuttered TheOilDrum.com blog, which closed shop in 2013 for mysterious reasons.

Steven Koonin & Unsettled: Cooking oil

/ Paul Pukite (@whut) / 1 Comment

Koonin who was chief scientist for BP, barely touches the elephant in the room (significant global oil depletion) in his anti-climate science diatribe “Unsettled: What Climate Science Tells Us, What It Doesn’t, and Why It Matters“. Checking Google Books for references to oil, it started out promising, thinking he would discuss why renewable energy was important, independent of any climate change considerations:

Page 3

Instead he discusses cooking oil, spread over several pages, in reference to a Richard Feynman parable on deceptive advertising.

Page 7
Page 10
Page 24

On page 33, a mention of crude (not cooking) oil, although the context is missing, perhaps referring to methane concentrations?

Page 33

But then back to cooking oil! Twice!

Page 119
Page 172

After 200 some pages, a few factual statements on supply and demand for fossil fuels and the difficulty of carbon capture.

Page 227
Page 243

That’s it. The book’s index only points to page 243 relevant to oil, which is consistent with Google Book’s search.

Index

The book is a smokescreen, with the intention of smearing climate science so as to avoid discussing the obvious No Regrets strategy for addressing rapidly declining oil reserves. No discussion of this on Rogan’s podcast with Koonin either. Oil companies do not want this discussed so they can continue to squeeze investment $$$ to find the meager and scant remaining reserves.

Koonin’s book is a bait and switch, which is to put the emphasis on the least existential crisis. Today we are globally using over 35 billion barrels of oil per year, but discovering less than 5 billion per year. That’s equivalent to having an annual income of $5,000 while spending as if you earn $35,000 — not close to sustainable after the savings you have runs out.