Ok so... For my "full-time" job I'm a water resources scientist who focuses on climate change. This is my field of study in Academia and we work on developing cutting edge technology for climate change adaptation and research.
The original article your article is referencing is here:
http://www.thebigwobble.org/2014/12/their-sky-has-changed-inuit-elders.html
I find this entire thing pretty ridiculous. Oh and also very "tinfoil hat" like. They claim there's "science" behind it and yet when I look into it further all I see is coincidences and data point outliers. I have yet to see solid evidence or even an equation in relation to the methodology.
To give you an idea of what's going on in Climate Change research... One of the leading groups for climate change research is the IPCC (Intergovernmental Panel on Climate Change). They perform an assessment every 5 to 7 years (publishing it as an Assessment Report) and gives a "snapshot" of what's going on plus parameters for research. One of the awesome things the Assessment Reports give is GCMs. GCMs, General Circulation Models, are "numerical models representing physical processes in the atmosphere, ocean, cryosphere and land surface". These are the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations (
Source).
We get a GCM for multiple scenarios, such as high CO2 generation scenario, low CO2 generation, etc because we don't know what it's going to be. Due to limitations with our current technology, climate change isn't deterministic, it's probabilistic. We can't say for certain "it's going to rain tomorrow". This is why the weatherman on the television says "there's a 15% chance it will rain tomorrow". This is a limitation of our current technology. Anyways, these models frequently run on a daily time-step usually to a max of 100 years (usually up to 2100) (sometimes monthly time-step if we're going longer or want to cut down on computational costs). Mostly because these models are physically based models (and not conceptual models), they require vast amount of data and require a large amount of computational power. However, the output is data with a resolution of 250 km by 250 km (this means the a VERY varied area is assumed to be an average value of a single square) depending on which GCM Model you're using (there's many ways with different equations, one may provide better data output for the United States whereas another provides better data output for China... there's a TON). Now when we're observing the effects of climate change globally that's all fine and dandy, but all the important questions require a much finer level of detail. Therefore, we can apply statistical downscaling methods or dynamical downscaling methods (You can find out the difference between the two here:
https://gisclimatechange.ucar.edu/question/63). These downscaling techniques (depending on what model you're using away) increases the resolution of the dataset, however again frequently requires extensive computational power. With the final downscaled data... we NOW perform our own disciplinary analysis (whether it be hydrology, climatology, etc.). As you can tell, this is VERY resource intensive... Something governments all around the world invest heavily on... We'll get back to the significance of this in a bit.
In addition, a colleague of mine's recent research involved comparing the accuracy between conceptual models and physical models (in terms of Hydrology and Climate Change studies). Just for definition purposes, a physical model is a model of the physical process and commonly requires a large amount of physical data whereas a conceptual model focuses on the concept of water balance and utilizes less historical data. My colleague's research states that a conceptual model, which is not as data intensive as the physical model, has a better accuracy when it comes to forecasts (predictions). Effectively saying that having more data can actually be harmful for climate change studies since future conditions are constantly changing and we can't predict the future. To give an analogy, why build a house for a small person when a big person could potentially move in? More than likely, it could be suggested that the "axis shift" may have such a marginal effect that it's not worth adding an additional parameter to our models (yes you may think that an axis shift could have a major effect but so far by our knowledge and technology this does not seem to be the case).
Now the reason why I went in-depth (well... that is really just the tip of the iceberg but it doesn't really do anything if I go into more detail for this topic) about what we do is to show how detailed we are at making sure the initial data values are... as right as possible. It's not perfect, and we lack the technology for even finer data resolution, however it's the best thing we have right now. Since so many resources are invested in generating the GCMs (which are by the way multi-million dollars projects) and since we (as an international community) are generating them fairly regularly (every 5-7 years) with different models... you see where I'm going with this... We want to make sure what we're doing is as right as possible. Therefore, each model is constantly tweaked and/or studied to try and see the best accuracy available. If the axis shift did affect climate change (in a massive way as depicted in the article), then we probably would have already added it into our data. This isn't some government conspiracy to "cover" something up. It's simply critical thinking.
Anyways, to sum it all up and to bring my brain garble to a conclusion.
1. It's probably not relevant to climate change studies
2. If it is, then it's effects are probably marginal.
3. It's probably outside the scope/scale of our climate models anyways
4. As a Climate Change researcher, this is pretty ridiculous and is very tin-foil-hat-like crazy.
If anyone wants more detail (since this is a research topic I absolutely enjoy and have fun working with) feel free to ask. However from the data I work with and from the research I do, I have yet to find this "wobble" to be relevant.
Note: I am paid for by my local government for climate change research. However, I was not paid to type up this messy post.
Edit: Came back to re-read this and... damn. Edited it a little bit plus added a little more content here and there.
