Amateur Hour

Three science philosophy papers and two recent findings seemed to fit together for this post.

Divergent Perspectives on Expert Disagreement: Preliminary Evidence from Climate Science, Climate Policy, Astrophysics, and Public Opinion :
James R. Beebe (University at Buffalo), Maria Baghramian (University College Dublin), Luke Drury (Dublin Institute for Advanced Studies), Finnur Dellsén (Inland Norway University of Applied Sciences)

“We found that, as compared to educated non-experts, climate experts believe (i) that there is less disagreement within climate science about climate change, (ii) that more of the disagreement that does exist concerns public policy questions rather than the science itself, (iii) that methodological factors play less of a role in generating existing disagreement among experts about climate science, (iv) that fewer personal and institutional biases influence the nature and direction of climate science research, (v) that there is more agreement among scientists about which methods or theoretical perspectives should be used to examine and explain the relevant phenomena, (vi) that disagreements about climate change should not lead people to conclude that the scientific methods being employed today are unreliable or incapable of revealing the truth, and (vii) that climate science is more settled than ideological pundits would have us believe and settled enough to base public policy on it. In addition, we observed that the uniquely American political context predicted participants’ judgments about many of these factors. We also found that, commensurate with the greater inherent uncertainty and data lacunae in their field, astrophysicists working on cosmic rays were generally more willing to acknowledge expert disagreement, more open to the idea that a set of data can have multiple valid interpretations, and generally less quick to dismiss someone articulating a non-standard view as non-expert, than climate scientists. ”

Any scientific discipline that lends itself to verification via controlled experiments is more open to non-standard views. It really is amazing how fast a new idea can be accepted when an experiment can be repeated by others. In climate science, no such control is possible, as the verification process can take years, while the non-standard views continue to proliferate. No wonder climate scientists get hardened to outsider views, as they have no way of immediately dismissing alternate interpretations.

On the other hand, astrophysics has a long history of being open to outsider opinion, with the amateur astronomer often given equal attention to a new finding. Two very recent cases come to mind.

These are two very concrete and objective findings that can be verified easily by others. So kudos to these two amateur sleuths for their persistence.

However, it's not so easy to verify that one is achieving verifiable progress in areas such as hydrology, as an ongoing debate series reveals that "Hydrology is a hard subject" (with a not to Feynman). The following paper tries to argue for a more open interpretation to the scientific process.

Every scientist borrows techniques from each column, but it is certainly true that the lack of being able to devise controlled experiments in climatology and hydrology places those researchers at a disadvantage compared to the lab-based researchers, or to incremental event-based discoveries (such as with astronomy).  Baker is almost suggesting  that a  better qualitative measure of holistic progress (uberty?) should take the place of a complete quantitative understanding.

And even if one could make sense of a hypothesized behavior, one still has to navigate the landmines of prediction versus a probabilistic forecast.

Stigma in science: the case of earthquake prediction
Helene Joffe, Tiziana Rossetto, Caroline Bradley, and Cliodhna O’Connor

Earthquake prediction is at a cross-roads, with a rather obvious debate going on at the USGS (and spilling over to other research groups) on whether lunisolar gravitational forcing can provide a significant trigger to the timing of an earthquake. Again, because of a lack of controlled experimentation, the argument can only take place in statistical  terms, and will take time and more observational data to resolve.

Yet, bottom-line, the question remains, who owns disciplines such as astrophysics, climate science, hydrology, and seismology?  To take as an example, the entire data set for the ENSO climate behavior can be reduced to the monthly time series of barometric pressure measured at only two locations, one in Tahiti and one in Darwin. This is as open to public interpretation as the amateur astronomers that scan the night-time skies for fresh discoveries. The three papers all say that experts disagree on how to solve or model the big geophysics problems.  I'd suggest that we allow the educated non-experts take a crack and listen to what they have to say.

Mathematical GeoEnergy book

We have finished a manuscript called "Mathematical GeoEnergy: Oil Discovery, Depletion, and Renewable Energy Analysis" which will be published by Wiley/AGU this December..

The editor is looking for potential reviewers, which they have asked me to supply. If anyone has any expertise on fossil fuels, geophysics, earth sciences, climate science, or renewable energy, send me an email puk@umn.edu, or leave a note in the comments, or tweet with the @whut address.

They don't necessarily need someone that will review the entire book, but could spend time on individual chapters that would cover a specific topic. The book essentially covers some of the topics of this blog, in particular the ENSO and QBO discussions, and also my previous blogs on fossil fuel depletion, as well as the http://PeakOilBarrel.com blog so that might pique some interest here.

The other aspect of the book is that it is targeted to graduate level studies, so it definitely hasn't been geared to a popular science market. However, it is almost all applied math and applied physics -- the intent is that the information can be used to help us through the energy transformation that the world is starting to dive into.

thanks, Paul Pukite

ESSOAr repository

The AGU created a new archiving site for preprints called ESSOAR

The Earth and Space Science Open Archive (ESSOAr) is a community server established to accelerate the open discovery and dissemination of Earth and space science early research outputs, including preprints and posters presented at major scientific meetings. In the future, other rich conference presentations may be added.

I submitted my AGU poster on ENSO and QBO to the site, and it appears that it is the first submitted along with a climate change preprint

The browse topic screen for latest submissions:

My presentation is here, tagged under a few topic categories
https://www.essoar.org/doi/abs/10.1002/essoar.b1c62a3df907a1fa.b18572c23dc245c9.1

Last post on ENSO

The last of the ENSO charts.

This is how conventional tidal prediction is done:

Note how well it does in extrapolating a projection from a training interval.

This is an ENSO model fit to SOI data using an analytical solution to Navier-Stokes. The same algorithm is used to solve for the optimal forcing as in the tidal analysis solution above, but applying the annual solar cycle and monthly/fortnightly lunar cycles instead of the diurnal and semi-diurnal cycle.

The time scale transitions from a daily modulation to a much longer modulation due to the long-period tidal factors being invoked.

Next is an expanded view, with the correlation coefficient of 0.73:

This is a fit trained on the 1880-1950 interval (CC=0.76) and cross-validated on the post-1950 data

This is a fit trained on the post-1950 interval (CC=0.77) and cross-validated on the 1880-1950 data

Like conventional tidal prediction, very little over-fitting is observed. Most of what is considered noise in the SOI data is actually the tidal forcing signal. Not much more to say, except for others to refine.

Thanks to Kevin and Keith for all their help, which will be remembered.

Correlation Coefficient of ENSO Power Spectra

The model fit to ENSO takes place in the time domain. However, the correlation coefficient between model and data of the corresponding power spectra is higher than in the time series. Below in Figure 1 the CC is 0.92, while the CC in the time series is 0.82.

Fig.1 : Power spectra of ENSO data against model

The model allows only 3 fundamental lunar frequencies along with the annual cycle, plus the harmonics caused by the non-linear orbital path and the seasonally impulsed modulation.

What this implies is that almost all the peaks in the power spectra shown above are caused by interactions of these 4 fundamental frequencies. Figure 2 shows a satellite view of peak splitting (also shown here).

Fig 2: Frequency sideband plot identifying components created by modulation of a biennial cycle with the lunar cycles (originally described here).

One of the reasons that the power spectrum gives a higher correlation coefficient — despite the fact that the spectrum wasn't used in the fit — is that the lunar tides are precisely determined and thus all the harmonics should align well in the frequency domain. And that's what is observed with the multiple-peak alignment.

Furthermore, according to Ref [1], this result is definitely not a characteristic of noise-driven system, and it also possesses a very low dimension of chaotic content. The same frequency content is observed largely independent of the prediction time profile, i.e. training interval.

References

1. Bhattacharya, Joydeep, and Partha P. Kanjilal. "Revisiting the role of correlation coefficient to distinguish chaos from noise." The European Physical Journal B-Condensed Matter and Complex Systems 13.2 (2000): 399-403.

NINO34 vs SOI

Experiment to compare training runs from 1880 to 1980 of the ENSO model against both the NINO34 time-series data and the SOI data. The solid red-curves are the extrapolated cross-validation interval..

NINO34

SOI

Many interesting inferences one can potentially draw from these comparisons. The SOI signal appears more noisy, but that could actually be signal. For example, the NINO34 extrapolation pulls out a split peak near 2013-2014, which does show up in the SOI data. And a discrepancy in the NINO34 data near 1934-1935 which predicts a minor peak, is essentially noise in the SOI data.  The 1984-1986 flat valley region is much lower in NINO34 than in SOI, where it hovers around 0. The model splits the difference in that interval, doing a bit of both. And the 1991-1992 valley predicted in the model is not clear in the NINO34 data, but does show up in the SOI data.

Of course these are subjectively picked samples, yet there may be some better combination of SOI and NINO34 that one can conceive of to get a better handle on the true ENSO signal.

click to enlarge

GC41B-1022: Biennial-Aligned Lunisolar-Forcing of ENSO: Implications for Simplified Climate Models

In the last month, two of the great citizen scientists that I will be forever personally grateful for have passed away. If anyone has followed climate science discussions on blogs and social media, you probably have seen their contributions.

Keith Pickering was an expert on computer science, astrophysics, energy, and history from my neck of the woods in Minnesota. He helped me so much in working out orbital calculations when I was first looking at lunar correlations. He provided source code that he developed and it was a great help to get up to speed. He was always there to tweet any progress made. Thanks Keith

Kevin O'Neill was a metrologist and an analysis whiz from Wisconsin. In the weeks before he passed, he told me that he had extra free time to help out with ENSO analysis. He wanted to use his remaining time to help out with the solver computations. I could not believe the effort he put in to his spreadsheet, and it really motivated me to spending more time in validating the model. He was up all the time working on it because he was unable to lay down. Kevin was also there to promote the research on other blogs, right to the end. Thanks Kevin.

There really aren't too many people willing to spend time working analysis on a scientific forum, and these two exemplified what it takes to really contribute to the advancement of ideas. Like us, they were not climate science insiders and so will only get credit if we remember them.

Derivation of an ENSO model using Laplace's Tidal Equations

Laplace developed his namesake tidal equations to mathematically explain the behavior of tides by applying straightforward Newtonian physics. In their expanded form, known as the primitive equations, Laplace's starting formulation is used as the basis of almost all detailed climate models. Since that's what they are designed to do, this post provides the details for solving Laplace's tidal equations in the context of the El Nino Southern Oscillation (ENSO) of the equatorial Pacific ocean. The derivation and results shown below essentially describe the framework of my presentation at this month's AGU meeting: Biennial-Aligned Lunisolar-Forcing of ENSO: Implications for Simplified Climate Models

The concise derivation for a model of ENSO depends on reducing Laplace's tidal equations along the equator. I could not find anyone taking a similar approach anywhere in the literature, even though it appears to be routinely obvious: (1) solve Laplace's tidal equations in a simplified context, then (2) apply the known tidal forcing and observe if the result correlates or matches the ENSO time series. In fact, it does, as I have shown before (and for QBO as well); but this is the first time that I have worked out the details in full for ENSO. Below is a two part solution.

Machine Learning and the Climate Sciences

I've been applying equal doses of machine learning (and knowledge based artificial intelligence in general) and physics in my climate research since day one. Next month on December 12, I will be presenting Knowledge-Based Environmental Context Modeling at the AGU meeting which will cover these topics within the earth sciences realm :

Table 1: Technical approach to knowledge-based model building for the earth sciences

In my opinion, machine learning likely will eventually find all the patterns that appear in climate time-series but with various degrees of human assistance.

"Vipin Kumar, a computer scientist at the University of Minnesota in Minneapolis, has used machine learning to create algorithms for monitoring forest fires and assessing deforestation. When his team tasked a computer with learning to identify air-pressure patterns called teleconnections, such as the El Niño weather pattern, the algorithm found a previously unrecognized example over the Tasman Sea."

In terms of the ENSO pattern, I believe that machine learning through tools such as Eureqa could have found the underlying lunisolar forcing pattern, but would have struggled mightily to break through the complexity barrier. In this case, the complexity barrier is in (1) discovering a biennial modulation which splits all the spectral components and (2) discovering the modifications to the lunar cycles from a strictly sinusoidal pattern.

The way that Eureqa would have found this pattern would be through it's symbolic regression algorithm (which falls under the first row in Table 1 shown above). It essentially would start it's machine learning search by testing various combinations of sines and cosines and capturing the most highly correlated combinations for further expansion.   As it expands the combinations, the algorithm would try to reduce complexity by applying trigonometric identities such as this

${\displaystyle \sin(\alpha \pm \beta )=\sin \alpha \cos \beta \pm \cos \alpha \sin \beta }$

After a while, the algorithm will slow down under the weight of the combinatorial complexity of the search, and then the analyst would need to choose promising candidates from the complexity versus best-fit Pareto front. At this point one would need to apply knowledge of physical laws or mathematical heuristics which would lead to a potentially valid model.

So, in the case of the ENSO model, Eureqa could have discovered the (1) biennial modulation by reducing sets of trigonometric identities, and perhaps by applying a sin(A sin()) frequency modulation (which it is capable of) to discover the (2) second-order modifications to the sinusoidal functions, or (3) it could have been fed a differential equation structure to provide a hint to a solution  .... but, a human got there first by applying prior knowledge of signal processing and of the details in the orbital lunar cycles.

Yet as the Scientific America article suggests, that will likely not be the case in the future when the algorithms continue to improve and update their knowledge base with laws of physics.

This more sophisticated kind of reasoning involves the refined use of the other elements of Table 1.  For example, a more elaborate algorithm could have lifted an entire abstraction level out of a symbolic grouping and thus reduced its complexity. Or it could try to determine whether a behavior was stochastic or deterministic.  The next generation of these tools will be linked to knowledge-bases filled with physics patterns that are organized for searching and reasoning tasks. These will relate the problem under study to potential solutions automatically.

High Resolution ENSO Modeling

An intriguing discovery is that the higher-resolution aspects of the SOI time-series (as illustrated by the Australian BOM 30-day SOI moving average) may also have a tidal influence.  Note the fast noisy envelope that rides on top of the deep El Nino of 2015-2016 shown below:

For the standard monthly SOI as reported by NCAR and NOAA, this finer detail disappears.  BOM provides the daily SOI value for about the past ~ 3 years here.

Yet if we retain this in the 1880-present monthly ENSO model, by simultaneously isolating [1] the higher frequency fine structure from 2015-2017, the fine structure also emerges in the model. This is shown in the lower panel below.

This indicates that the differential equation being used currently can possibly be modified to include faster-responding derivative terms which will simultaneously show the multi-year fluctuations as well as what was thought to be a weekly-to-monthly-scale noise envelope. In fact, I had been convinced that this term was due to localized weather but a recent post suggested that this may indeed be a deterministic signal.

Lunisolar tidal effects likely do impact the ocean behavior at every known time-scale, from the well-characterized diurnal and semi-diurnal SLH tides to the long-term deep-ocean mixing proposed by Munk and Wunsch.  It's not surprising that tidal forces would have an impact on the intermediate time-scale ENSO dynamics, both at the conventional low resolution (used for El Nino predictions) and at the higher-resolution that emerges from SOI measurements (the 30-day moving average shown above).  Obviously, monthly and fortnightly oscillations observed in the SOI are commensurate with the standard lunar tides of periods 13-14 days and 27-28 days. And non-linear interactions may result in the 40-60 day oscillations observed in LOD.

from Earth Rotational Variations Excited by Geophysical Fluids, B.F. Chao, http://ivs.nict.go.jp/mirror/publications/gm2004/chao/

It's entirely possible that removing the 30-day moving average on the SOI measurements can reveal even more detail/

Footnote

[1] Isolation is accomplished by subtracting a 24-day average about the moving average value, which suppresses the longer-term SOI variation.