Two Russian climate scientists going after each other (perhaps one-sided) with the referee breaking it uphttps://t.co/PklkG3MJZ6 pic.twitter.com/5gto7qB7mk

— Paul Pukite đ (@WHUT) September 9, 2020

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In the context of my original comment, this is an almost predictable back-and-forth discussion. As I implied, in the absence of some actual data to compare against, the mathematical degrees of freedom involved in a fluid dynamics formulation will allow many possibilities for a potential solution.

Geoscientific model development should be geared toward emulating some known physical behavior, otherwise it would be just discussion of the math underlying partial differential equations. Consider the figure below taken from https://doi.org/10.1029/2019JD032362 which collects over 30 model comparisons to a historical QBO time-series, yet revealing little consilience and limited agreement among the models. The authors of the paper suggest that even though *“the number of climate or Earth system models being able to simulate the QBO”* …. *“However, the quality of the simulation of the QBO has not improved.”*

What can end this back-and-forth discussion is the mathematical insight leading to a formulation that will allow the QBO time-series to be modeled effectively. Otherwise, in the absence of a real-world context, there is no end in sight.

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]]>My comment:

“Entertaining reading Willard pontificating on the way SOTA experimental research proceeds. The fact is that if you are involved in a truly productive research discipline, replication of experiments is almost always replaced by leapfrogging of results by competing teams. No one is going to waste their time replicating someone elseâs experiment when they can one-up the ante with a more advanced result or a new finding, especially when you promised your funders that the $4M experimental apparatus that they paid for wasnât going to be used for making new discoveries.

I like Victorâs âontology of reasonsâ approach. I was recently involved in a project that applied concepts from the semantic web (ontology applied to the WWW as advocated by Berners-Lee) to organize scientific knowledge for engineering and modeling applications

https://ui.adsabs.harvard.edu/abs/2017AGUFMED23D0326P/abstract

I can readily see how the Citation Typing Ontology could be applied, with the is_extended_by object property representing much of how science advances.”

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]]>“… there is a second major wildlife population cycle. In addition to the ~4-year lemming/vole cycle, there is a ~10-year cycle of snowshoe hare, Canada lynx and ruffed grouse populations in North America and the autumnal moth in Fennoscandia that has been strongly correlated with the 9.3-year half lunar nodal cycle for over 150 years. In some of your blogs, the matchup you made between my lemming cycle figure and your ENSO model data is very impressive. So I thought it would be interesting to create a similar figure of the hare/grouse cycle (data from my 2014 10-year cycle paper) and see whether you might relate this cycle to some of your data.”

Best regards, Herb

Herb,

That 9.3 and 18.6 cycle is impressive for the the years displayed. I couldn’t add anything more to it if you are thinking that it’s mainly related to the well-known moonlight cycle based on lunar declination that you describe in the paper. It’s a slight effect in terms of absolute declination but it may make a difference considering how predator & prey nocturnal eyesight has evolved.

The other ~4 year cycle may be more related to climate cycles, set according to how lunar tides interact with the annual cycle.

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]]>Hu, S., et al (2020). Improved ENSO prediction skill resulting from reduced climate drift in IAPâDecPreS: A comparison of fullâfield and anomaly initializations. Journal of Advances in Modeling Earth Systems

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001759

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]]>Fun times in open science publishing

Editor : "Please be nice"

Reviewer : "Certainly" pic.twitter.com/VlU5qBHb4C— Paul Pukite đ (@WHUT) September 17, 2020

As pertains to my original comment in that reviewing thread, the mathematical formulation is endlessly arguable unless it is put to use and compared to actual data, as that is what a “model” implies. One side of this argument will prove more correct than the other when that comparison is made.

The reviewer is coauthor on this paper on triadic interactions “Energy cascade in internal wave attractors”

https://www.sciencedirect.com/science/article/pii/S2210983817300184

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]]>But look, tweeps, the reality is we live in a world of finite resources. We have to carefully decide how to invest those resources so that we can continue making new discoveries and learning about the universe. If we invest too much on a dead horse, that's game over.

— Sabine Hossenfelder (@skdh) September 14, 2020

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]]>Statistical physics approaches to the complex Earth system

Jingfang Fan, Jun Meng, Josef Ludescher, Xiaosong Chen, Yosef Ashkenazy, Jurgen Kurths, Shlomo Havlin, Hans Joachim Schellnhuber

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as, critical phenomena, network theory, percolation, tipping points analysis, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics approaches can be useful in the field of Earth system science.

https://arxiv.org/abs/2009.04918

3 Applications 38

3.1 Climate System . . . . . . . . . . . . . . . . . . . . . . . 38

3.1.1 El NiĂ±oĂąÄĆSouthern Oscillation . . . . . . . . 40

3.1.2 Indian Summer Monsoon . . . . . . . . . . . . . 52

3.1.3 Extreme Rainfall . . . . . . . . . . . . . . . . . . . . . 56

3.1.4 Atmospheric Circulation and Global Warming . . . . . . . 60

3.1.5 Atlantic Meridional Overturning Circulation . . . . . . . . . 67

3.2 Earth Geometric Surface Relief . . . . . . . . . . . . . . . . . . .71

3.2.1 Self-similarity and Long-range Correlations . . . . . . 71

3.2.2 Landmass and Oceanic Clusters . . . . . . . . . . . . . . . 72

3.2.3 Origin of the Discontinuity . . . . . . . . . . . . . . . . . . . . . 74

3.3 Earthquake Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . .77

3.3.1 Scale Invariance and the inter-event Distribution . . . . 77

3.3.2 Modeling Seismic Time Series by the Point Process Approach . . . 80

3.3.3 Memory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.3.4 Earthquake Forecasting . . . . . . . . . . . . . . . . . . 86

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]]>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2791570/

SST anomalies averaged over the NiĂ±o3.4 region (see Fig. 1) and those of the reconstructed ENSO mode, which explains 35% of the total variance. SST in this region is key for large-scale oceanâatmosphere interactions important to ENSO. The reconstructed ENSO mode is determined from Singular Spectrum Analysis. Data are HadISST 1.1 (41).

The other modes are the TIW, right?

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]]>Carbon Brief post

https://www.carbonbrief.org/guest-post-why-does-land-warm-up-faster-than-the-oceans

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