What atmospheric phenomenon am I seeing in this photo?

You are seeing an older jet contrail and its shadow. You can also see some newer contrails, and maybe even some older ones.

Contrails are not necessarily continuous. For example, the contrail that appears to be horizontal in the image below appears to magically appear near the left end of the image.

That's what you are seeing in your image, but in this case the contrail in question appears to be vertical. The contrail stops about 1/3 the way up the picture. Given that the contrail gets thicker with increasing height up the picture, and becomes very diffuse near the top, I suspect that where the contrail stops is the youngest part of the contrail. That would mean the plane was flying away from your position (i.e., toward the horizon visible in the picture). The contrail stops at that point because the plane entered a region of dry, stable air. Contrails persist for seconds to several hours, depending on the local atmospheric conditions.

What about the dark band below the contrail? That's the contrail's volumetric shadow. You were in just the right place to see it. Notice that while the contrail is twisted and not quite straight, that dark band is straight. If you look closely, you can see that it doesn't end at the horizon. It appears to extend into the water. It's a shadow, a volumetric shadow to be precise.

The next two links are to images that show exactly this effect. These images appear to be copyrighted, so I'm going to leave them as links rather than embed them.

Link to image portraying a jet chasing it's volumetric shadow

Link to another image of a contrail and its volumetric shadow

Finally, this is a very striking example of the volumetric shadow from a contrail, in this case the launch of STS-98. This image is from Wikipedia, so it's fair use. In other images (you can search for them) taken from other angles, the shadow isn't nearly as striking, sometimes even non-existent.


How does the Coriolis effect affect rivers and estuaries

The effects of Coriolis in rivers and estuaries are more subtle than in the open ocean. Coriolis tends to be a second or third order process in fast-moving and relatively small systems like rivers.

On the other hand, when the residual flow in an estuary is considered, then that process tends to be slow and persistent and the effects of Earth's rotation (Coriolis) can be more obvious. The residual flows tend to be much weaker than tidal flows in estuaries. Both seaward and landward residual flows are going to be slightly displaced to the right of the direction of flow (in the northern Hemisphere) resulting in asymmetric flows. The resulting exchange flow (in the northern Hemisphere), looking in up-estuary direction, is going to have denser seawater flowing into the estuary in the deeper right side of the estuary, while fresher (lighter) river water will tend to flow seaward near the surface along the left side of the estuary.

 Source GSO-URI.

The asymmetric flow exchange will affect any particle in suspension (e.g., organisms, sediment). In general, estuarine circulation tends to create areas of high turbidity (estuarine turbidity maximum, ETM) in the vicinity of the tip of the salt wedge. Decomposition of organic material in the high turbidity zone of the ETM can cause oxygen depletion, especially in the lower part of the vertical where mixing with oxygen-rich surface water is inhibited by stratification. In a system where Coriolis is a relevant factor, the distribution of this anoxic conditions (the often called "dead zone") is going to be displaced and can be more extensive in the right side of the estuary.

 Source: Columbia River Estuarine Turbidity Maxima

A good summary of the effects of rotation (Coriolis) in estuaries can be found in Valle-Levinson (2011).

  Valle-Levinson A (2011)  Large Estuaries (Effects of Rotation). In: Wolanski E and McLusky DS (eds.) Treatise on Estuarine and Coastal Science, Vol 2, pp. 123–140. Waltham: Academic Press.


These are weakly formed gravity clouds or density clouds. Clouds are formed from condensation, which is just tiny water (and other gases) droplets and vapor. As such, these tiny droplets and vapors will flow like ripples on the surface of water in a slow breeze. What you're seeing here is light-action wave ripples in the atmosphere and the resulting effect on the tiny droplets, is a wave-like or ripple-like pattern.


How was the onset of the Cambrian period dated?

Fossils are only in very rare cases dated directly, because they do not contain sufficient radioactive isotopes for dating.
Most geological periods are first and foremost defined by biostratigraphy- fossil assemblages, typically.
The Cambrian period starts with the Fortunian stage - this stage is defined by the appearance of a certain trace fossil in Fortuna peninsula, eastern Canada, but is correlable with similar formations world wide. The whole discussion on how this stage is defined is available here.
As outlined in this paper, in addition to fossils, the Fortunian stage hosts various volcanic rocks which contain zircons, which can be dated by U-Pb. The age of the actual boundary is then interpolated from these data.


What is the cause of temporary smell and black color of underground water?

This is due to the presence of iron sulfide in the water. The iron sulfide oxidizes and goes into solution. Ferrous iron oxidizes to ferric iron. Depending on the water chemistry, this commonly precipitates out as orange-coloured iron oxyhydroxide, but that does not seem to be occuring in your case, otherwise you would see it. When it does happen, the pH of the water drops, as seen in acid mine drainage.

The sulfide may be volatilized as hydrogen sulfide which smells, or it too may oxidize and volatilize as sulfur dioxide (rotten egg smell). It can continue to oxidize to sulfate.

The iron sulfide may be present in the aquifer or it may be formed by microbial activity in your well. In the latter case, treating the well with bleach can solve the problem, at least temporarily. Be sure to treat all the piping, too. If possible, install a backflow preventer so water doesn't flow back down the well.


How should one compare the comfort of climates?

One measure that could be used is the apparent temperature, which combines air temperature, humidity and wind speed to derive the temperature that the human body feels. It is sometimes referred to as the "feels like" temperature or the Steadman temperature, after Robert Steadman who proposed the idea in 1971.

The formula used by the National Oceanic and Atmospheric Agency, for calculating the apparent temperature is:

 AT = -2.7 + 1.04*T + 2.0*e -0.65*v

where AT and T (air temperature) are °C, e is vapor pressure in kPa,
and v is 10m wind speed in m/sec.

Most weather oraganizations include the calculated apparent temperature in their weather reports.


Why does NW coast of Scotland look like it does? (Similar to Norway, so different to other European coastlines)

These are actually a geological feature called "fjord" which the Scandinavian Peninsula and surroundings (including Scotland) are known for. It is the result of glacial erosion during the Ice Ages. See more info here: Fjord (Wikipedia).


Locating shearlines or deformation zones in a map

I am not able to give full actual code in Python but I hope I can point you in the right direction if I were to go about calculating diffluence and confluence.

One can start with making a streamlines plot using matplotlib first. Once you have that you need to get the actual streamlines data as described in this link -
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.streamplot.html and that data structure is given as LineCollection.

For each streamline in that LineCollection you will need to calculate tangential and normal vectors based on the idea of a localized Cartesian system with a origin centered on somewhere along the streamline.

Once you have the tangential and normal vectors you will need to use this equation i.e. the divergence in natural coordinates

$$ \nabla . v_h = (e_n \frac {\partial e_s}{\partial n} * v_h) + \frac {\partial v_h}{\partial s}$$

Using a combination of Frenet Serret formula this can be shown to be equal to

$$\frac {|v_h|}{R_n} + \frac {\partial v_h}{\partial s} $$

From your perspective you only want the first term on that equation to calculate diffluence and confluence. So $R_n$ is the radius of curvature of the normal vector (which you already calculated before). So in order to calculate the radius of curvature you can use this python code for least squares fitting (using the approach of least squares - obtain the ellipse and then calculate the radius). If the radius is  positive i..e $R_n >0 $ then you have an area of diffluence and negative then confluence.

UPDATE

If you are looking for the exact NCL shear stretch deform in Python what you can do is to use this package in python pyspharm. The data has to be global and then you need to orient the grid in a south to north direction. Once you have that you can use that package to calculate the shear, stretch and deformation parameters by using the pyspharm's getgrad() method to calculate the vector gradient of the wind vectors. If you pass in the components of the wind vectors(both u and v) to getgrad() you will get the zonal and meridional components of the gradient of the wind vectors. Adding and subtracting them will get you the shear and stretch.

Sample code

 from spharm import Spharmt

 uspectral = Spharmt.grdtospec(u,ntruncation)
 vspectral = Spharmt.grdtospec(v,ntruncation)
 dudx,dudy = Spharmt.getgrad(uspectral)
 dvdx,dvdy = Spharmt.getgrad(vspectral)
 #shear and stretch
 shear = dvdx + dudy
 stretch = dudx - dvdy


This is a complex question, and I cannot give you a complete answer I'd like to point out that there is a very important article published in February of this year on the subject: volcanoes are in an eruptable state very very short periods of time in the geologic timescale; the mobilization of magma is very rapid but its storage is kept near, but under the solidus (where rock melts). Surely there will be more studies on this.

Since the reservoir of Yellowstone is very large, the chances of having enough eruptable magma to cause Yellowstone to detonate is low, hence why its eruptions are on the time scale of hundreds of thousands of years (very small in terms of geologic time).

The second thing I would like to address is "Hot Spot". The Yellowstone super volcano is supplied by the bifurcation of the Farallon slab as it subducts underneath the North American plate. This bifurcation causes the separated plate to sink displacing hot mantle upwards to the surface causing volcanism (Yellowstone). There isn't a clear definition of what scientists want to call it, often suggested to be a Plume or Hot Spot. It is not migrating. Yellowstone will eventually erupt. It is still Active.

Sorry I could not give you a better answer, but hopefully this illustrates some ideas that might explain why Yellowstone has a long eruption cycle compared to smaller volcanoes.


Gaussian wavelet generation with a given frequency

If I understand the question, I don't think you can do what you want to do. The spectrum of a Gaussian time series must contain frequencies down to DC, i.e. very low frequencies (Python code from scipy.org):

A wavelet whose spectrum is a Gaussian is called a Ricker wavelet, or sometimes Mexican Hat wavelet. I often use this wavelet to model seismic reflection data. It has a central frequency, and is bandlimited. As such, the wavelet oscillates around zero amplitude — it does not have a DC component:


Will a country that cares for its environment be affected by climate change due to other countries' pollution?

The main driver for anthropogenic climate change is carbon dioxide because it is very long-lived and hence relatively evenly distributed within the atmosphere. And even if you'd somehow manage to keep reduced levels of $CO_2$ in your country's atmosphere, you'd still be affected by large-scale weather changes. So yes, your country would be affected just like any other country.

Of course other benefits from climate-friendly policies, such as better air quality due to reduced combustion or better public transport due to investments in that sector, have a stronger local impact.


How did New Zealand's continental crust get split between two plates?

The continental crust and shelf of new zealand is one peice that has a fault running through it due to it running over a boundary between two oceanic plates. Continental crust cannot really* subduct due to its lower density so if forced onto a subduction zone they will actually slide over it onto the other plate. It helps of you remember oceanic crust and continental crust can exist on the same plate but have different properties.

Something similar has/is happening in North America the san andreas fault is caused by part of the north american continent running over the old east pacific (farallon) plate and spreading center, so now the portion attached to the north american plate is being dragged in one direction and peice sitting on/in the (west) pacific plate is moving in a different one. it is slowly pulling the continent apart and eventually may create an island like new zealand.

Here is more in depth discussion of strike-slip/transcurrent plate boundaries.

*small pieces can get dragged down but not much.


What are the formula weight and gram-formula unit (gfu)?

$M_i$ is the molar mass of the ith element. Dividing $w_i$ by $M_i$ yields the number of moles of that element in the sample.

There are two primary ways of looking at abundances. One is by weight (or mass), the other is by number of atoms. Consider beryllium and uranium. Their abundances are very similar when looked at in terms of mass: 1.9 parts per million (ppm) for beryllium versus 1.8 ppm for uranium. In terms of molar abundance, they aren't so similar. In this sense, beryllium's abundance is 4.3 ppm; for uranium, it's only 0.15 ppm.


During the "Ice Ages" or "Snowball Earth" times, where was all the energy?

I'm not quite sure if the question is asking about glacial, ice ages, or snowball Earth, and whether it's about the onset or end of a glacial period. I'll try to hit all three.
Ice Ages and Milankovitch Cycles
Ice ages are long spans of time that marked by periods of time during which ice reaches far from the poles, interspersed by periods during which the ice retreats (but never quite goes away). The periods of time during which ice covers a good sized fraction of the Earth are called glacials; the periods during which ice retreats to only cover areas in the far north and far south are called interglacials. We are living in ice age conditions, right now. There's still ice on Antarctica and Greenland. We are also in an interglacial period within that larger ice age. The current ice age began about 33 million years ago while the current interglacial began about 11,700 years ago.
The Milankovitch cycles determine whether the Earth is in a glacial or interglacial period. Conditions are right for ice to form and spread when precession puts northern hemisphere summer near aphelion and winter near perihelion and when both obliquity and eccentricity are low. The Earth currently satisfies the first of those conditions, but obliquity and eccentricity are a bit too high. That makes our northern hemisphere summers are a bit too warm, our winters a bit too cold.
The Milankovitch cycles] provides several answers to the question "where does the energy go?" Those times when conditions are ripe for glaciation have energy in the northern hemisphere spread more uniformly across the year than times not conducive to glaciation. Summers are milder, which means accumulated snow doesn't melt as much. Winters are milder, which means more snow falls.
Once ice does become ubiquitous, another answer to the "where does the energy go" question is into space. Ice and snow are white. Their presence reduces the amount of sunlight absorbed by the Earth.
The first paper listed below by Hays et al. is the seminal paper that brought the concept of Milankovitch cycles to the forefront. The second paper by Abe-Ouchi et al. dicusses a recent climate simulation that successfully recovers many salient features of the most recent glaciations. Most importantly, this paper appears to have solved the 100,000 year mystery and shows why deglaciation operates so quickly.

Hays, J. D., Imbrie, J., & Shackleton, N. J. (1976, December). "Variations in the Earth's orbit: Pacemaker of the ice ages." American Association for the Advancement of Science.
Abe-Ouchi, A., Saito, F., Kawamura, K., Raymo, M. E., Okuno, J. I., Takahashi, K., & Blatter, H. (2013). "Insolation-driven 100,000-year glacial cycles and hysteresis of ice-sheet volume." Nature, 500(7461), 190-193.
Icehouse Earth vs Greenhouse Earth
The Earth's climate appears to have been toggling between two climate extremes for much of the Earth's existence, one where things are cold and ice is likely to form and the other where ice is absent worldwide except maybe right at the poles. Dinosaurs roamed the arctic and Antarctica when the Earth was in one of its greenhouse phases. The Earth has been in a greenhouse phase for most of the Earth's existence.
Milankovitch cycles don't cause glaciation during greenhouse Earth periods. Ice ages happen when the Earth is in an icehouse phase. What appears to distinguish greenhouse and icehouse phases are the positions and orientations of the continents. Having a continent located over a pole helps cool the climate. Having continents oriented so they channel ocean circulation in a way that keeps the ocean cool also helps cool the climate.
The Earth transitioned from its hothouse mode to its icehouse mode 33 million years ago or so. That's right about when two key events happened in the ocean. Up until then, Antarctica was still connected to both Australia and South America. The separation from Tasmania formed the Tasmanian Gateway, while the separation from South America formed the Drake Passage. This marked the birth of the very cold Southern Ocean, it marked the buildup of ice on Antarctica, and it marked the end of the Eocene.

Bijl, P. K., Bendle, J. A., Bohaty, S. M., Pross, J., Schouten, S., Tauxe, L., ... & Yamane, M. (2013). "Eocene cooling linked to early flow across the Tasmanian Gateway." Proceedings of the National Academy of Sciences, 110(24), 9645-9650.
Exon, N., Kennett, J., & Malone, M. Leg 189 Shipboard Scientific Party (2000). "The opening of the Tasmanian gateway drove global Cenozoic paleoclimatic and paleoceanographic changes: Results of Leg 189." JOIDES J, 26(2), 11-18.
Snowball Earth and the Faint Young Sun Paradox
Snowball Earth episodes were not your average ice age. Ice typically doesn't come near the tropics, even in the worst of ice ages. Snowball Earth means just that; the snow and ice encroached well into the tropics, possibly extending all the way to the equator.
The problem with snowball Earth isn't explaining where all the energy went. The real problem is explaining why the ancient Earth wasn't in a permanent snowball Earth condition, starting from shortly after the Earth radiated away the initial heat from the formation of the Earth.
The solar constant is not quite constant. While it doesn't change much at all from year to year, or even century to century, it changes a lot over the course of billions of years. Young G class stars produce considerably less energy than do middle aged G class stars, which in turn produce considerably less energy than do older G class stars. When our Sun was young, it produced only 75% or so as much energy than it does now.

Image source: http://en.wikipedia.org/wiki/File:Solar_evolution_(English).svg.
By all rights, the Earth should have been completely covered with ice. The young Sun did not produce enough energy to support open oceans. This obviously was not the case. There is plenty of geological evidence that the Earth had open oceans even when the Earth was quite young.
This 40 year old conundrum, first raised by Carl Sagan and George Mullen, is the faint young Sun paradox. There have been a number of proposed ways out of the paradox, but none of them quite line up with the geological evidence.
One obvious way out is that the Earth's early atmosphere was very different from our nitrogen-oxygen atmosphere and contained significantly more greenhouse gases. The amount of greenhouse gases needed to avert a permanent snowball Earth is highly debated, ranging from not much at all to extreme amounts. Another way out is a reduced albedo due to the significantly smaller early continents and lack of life. The young Earth would have been mostly ocean, and ocean water is rather dark (unless it's covered with ice). Lack of life means no biogenic cloud condensation nuclei, which means fewer clouds.

Goldblatt, C., & Zahnle, K. J. (2011). "Faint young Sun paradox remains." Nature, 474(7349), E1-E1.
Kienert, H., Feulner, G., & Petoukhov, V. (2012). "Faint young Sun problem more severe due to ice‐albedo feedback and higher rotation rate of the early Earth." Geophysical Research Letters, 39(23).
Rosing, M. T., Bird, D. K., Sleep, N. H., & Bjerrum, C. J. (2010). "No climate paradox under the faint early Sun." Nature, 464(7289), 744-747.


Why are solar longwave and terrestrial shortwave radiations neglected in radiation balance models?

It is because longwave (LW) correspond to a negligible part of the solar radiation and shortwave corresponds to a negligible part of solar radiation.

You can make an experiment yourself using NASA's Radiance calculator. By adjusting the parameters for Earth's and the Sun you will get the following plot of energy flux at different wavelengths

(The red line is the radiation spectra of the Sun and the blue line that of the Earth)

The cool thing about this tool is that you can see the actual data (or download it as a spreadsheet) and estimate the total energy flux between any range of wavelength (the area under the curve).

In this case, for SW radiation (0.1 - 4 $\mu m$) and LW radiation (4 - 100 $\mu m$) you get the following numbers

Sun LW: $1.98 \times 10^7 W m^{-2} {sr}^{-1}$
Sun SW: $1.95 \times 10^5 W m^{-2} {sr}^{-1}$
Sun Total: $2.00 \times 10^7 W m^{-2} {sr}^{-1}$

Earth LW: $1.24 \times 10^2 W m^{-2} {sr}^{-1}$
Earth SW: $1.82 \times 10^{-1} W m^{-2} {sr}^{-1}$
Earth Total: $1.23 \times 10^2 W m^{-2} {sr}^{-1}$

So in terms of percentages:

Sun: 99.03% SW and 0.97% LW

Earth 0.15% SW and 99.85% LW

That illustrate my initial point: LW is negligible for the Sun and SW negligible for Earth. Therefore, in simple models you can ignore both solar LW and terrestrial SW.

And why it is that way?

Well the reason is because the peak of the emissions decrease inversely proportional to temperature (according to Wien's law), while the total power grows MUCH faster, proportional to the fourth power of the temperature (according to Stefan-Boltzmann law). So the Sun been much hotter, produce an enormously larger amount of energy per square meter, but most of that energy is delivered in much shorter wavelength.

Note: It is interesting to note that despite that it is OK to neglect solar LW it is still 1580 times stronger than terrestrial LW.


If you check the 6-hourly products in the links you shared, you can see that CFSR includes 50 variables in the "complete" dataset. The hourly dataset has a limited number of variables (25 in total). That's what they mean by "selected". Hope this help.


What is the predictability of El Niño/La Niña?

All season forecast systems are subject to uncertainty. The uncertainty arises from an imperfect initial state, such as initial conditions, and from imperfect models, such as uncertainty due to, numerical methods, parametric models, data sampling. Forecasting systems utilize ensembles and their spread to quantify uncertainty.

See the spread in forecasts for El Niño produced by a number of international models:

When considering large scale processes varying on interannual scales ENSO, which is the coupled process of El Niño/La Niña SST anomalies and the Southern Oscillation, is relatively well predictable. However, the fidelity of the forecasting is modulated by the season, the phase and intensity of ENSO. For example, there is a decrease in skill during the so called "spring predictability barrier" where, during the boreal spring when SST anomalies are abnormally variable. Model drift (such as SST drift as the forecast progresses) and bias especially for a couple-model such as the ENSO, is a very real issue. After only a season the bias inherent in the results renders the forecasting unreliable to the point where the model may be unable to recognize different types of ENSO (such as classical East Pacific versus central Pacific ENSO events).

So unfortunately at this time your specific question is still very hard to answer with any degree of satisfactory precision. But at least we know that it is not more than one season in the future.

Source:

Alves, Oscar, et al. "Seasonal and decadal prediction." Operational Oceanography in the 21st Century. Springer Netherlands, 2011. 513-542.


Why does computation cost increase 24 times when the spatial resolution doubles?

I can't answer why there's a 24x increase, according to the textbook - but it may well be the case for a specific model. In general,

If you double the resolution of a 3D model in each dimension, you are multiplying the number of cells (or elements) in the model by $2^3 = 8$.

Typically the maximum length of the time step that a model may be run at and remain stable is proportional to $\frac{\Delta x}{u}$, where $\Delta x$ is the size of a grid cell and $u$ is the speed of the flow in that cell (see info on the CFL condition) (for simplicity I'm assuming that the flow is parallel to one grid axis, which makes this one-dimensional; but the same result applies if not). This means that if you double the spatial resolution, you will probably also have to halve the timestep to maintain numerical stability. This gives an extra factor of two on the computation time, taking us to $2^4 = 16$ times.

This will vary in its details from model to model, and it is entirely feasible that additional non-linear processes in a given model push that to 24 times.


Measuring bed dip angle from contour lines, which lines to pick?

You are correct by saying that the dip angles vary greatly depending on the contours that you pick. In contrast to artificial maps designed to teach basic structural geology to students (such as the one you added in your question), natural maps are rarely "nice and friendly" regarding dip calculation.

There are several reasons for this:

The dip angle may actually vary. In artificial maps, the beds are usually tabular and not folded (unless this is a folding exercise). Natural rocks are rarely exactly planar and tabular, and their thickness also varies. This can result in different dips for the top and the bottom of the bed at the same location.
Maps are not usually drawn with dip calculations in mind. The contacts are based on the limited exposures available to the mapping geologist. The contacts between rock units may be obscured by vegetation, talus, soil and even houses and roads. Making a map that is dip-calculation friendly is time consuming, if possible at all.
It also depends on the accuracy of the topographic contours. As you probably know, you have to take the elevation into account in these calculations. Even if the contours are accurate, the topography may be not accurate or not spaced properly. I can testify from my own experience that the topographic map that was available to me of this area:
 seems like we are walking on a somewhat planar area without any steep slopes and only several meters of variability in elevation.

So what are your options? Some maps already have dip measurements built into them, thanks to the detailed work of the mapping geologist. If not, some structural maps of the same area may be available from the local geological survey/society. If you insist in making your own calculations, and the bed is rather planar, you can try to use a wider area for calculation (let's say 1 km instead of 100 m). You can try to take many measurements over an area and make a histogram of the measurements, or maybe examine the spatial distribution.


There have been a number of studies based on observations and modelling of the Antarctic Sea Ice trends.  One major observation is that since continuous satellite coverage began in late 1978 (3), there has been an increase in annual Antarctic sea ice extent (1)(2)(3)(4), that reach ~28% of the Arctic sea ice loss (1).

Critically, according to Simmonds (2015) (1) (and Fan et al. 2014 (3)), that despite interannual, sub-decadal and multidecadal variations, there is a statistically significant increasing trend in sea ice extent for all seasons since late 1978 (As shown in the image below).

Caption (from the NSIDC): Arctic and Antarctic Sea Ice Extent Anomalies, 1979-2012: Arctic sea ice extent underwent a strong decline from 1979 to 2012, but Antarctic sea ice underwent a slight increase, although some regions of the Antarctic experienced strong declining trends in sea ice extent. Thick lines indicate 12-month running means, and thin lines indicate monthly anomalies.

Essentially, the sea ice extent annual trend is at record-levels since records began with continuous satellite coverage (from late 1978).

As to why this trend is occurring, there is a considerable amount of uncertainty (1)(2)(3)(4), particularly in reconciling modeled sea ice with the extent observed (1).  Simmonds (2015) (1) suggest that this could just be a consequence of the different dynamics between the Arctic and Antarctic - i.e. one model does not fit both ice caps.

Observations documented by Fan et al (2014)(3) indicate that since 1979, there has been a decrease in sea surface temperatures (SST) and surface air temperatures around Antarctica, with the exception of the Antarctic Peninsula and surrounding seas. The trends indicate that in the southern summer (December-February) there is a trend of SST dropping by 0.6°C corresponding to a 9% increasing trend in sea ice extent during the same time - this is also noted by Simmonds (2015) (1) as being the greatest trend in sea ice extent.  This is accompanied by intensified westerly winds and a strengthened meridional sea level pressure gradient, both in the southern summer and in the annual mean.

Fan et al. (2014) (3) suggest that there is evidence for a multidecadal climate variability regime occurring in the region - this stands to reason considering that the time of observations of sea ice extent has only been ~35 years.

There have been several theories suggested in the literature, below are a few of them:

Fan et al. (2014) (3) describe an earlier idea that the increase in greenhouse gases and ozone depletion may cause an increase in the increased westerly winds, there is considerable uncertainty in the interaction between these factors (4).
Models from Holland et al. (2014) (4) suggest a link between Antarctic-wide ice thickness and area trends contributing to overall sea ice trends.
Related to the models above, several papers suggest that the mechanism is related to increases in meltwater fluxes from the Antarctic continent itself (2)(3).  The meltwater flux is modelled to have increased during the southern summer, depositing greater amounts of cool freshwater into the surface layers of the surrounding Southern Ocean, resulting in cooler SSTs in the southern summer, potentially causing the increase in summer sea ice extent trend.
However, most critically, Holland et al. (2014) (4) note that the freshwater 'extraction' by sea ice is a small percentage of the actual fresh meltwater flux. Instead they model that instead of freshwater flux, the driver is based on trends of Antarctic ice thickness and area.

This means that the increase in sea ice is not a 'counter balance' for the ice melted from the continental ice sheets, but rather, according to the models, the increasing sea ice extent appears to be a symptom of the continental ice sheet melting.

References

(1) Simmonds 2015 Comparing and contrasting the behaviour of Arctic and Antarctic
sea ice over the 35 year period 1979–2013 Annals of Glaciology

(2) Bitanja et al. 2015 The effect of increased fresh water from Antarctic ice shelves on
future trends in Antarctic sea ice Annals of Glaciology

(3) Fan et al. 2014 Recent Antarctic sea ice trends in the context
of Southern Ocean surface climate
variations since 1950 Geophysical Research Letters

(4) Holland et al. 2014 Modeled Trends in Antarctic Sea Ice Thickness, Journal of Climate (Full text here)


How do I interpret this "sky cover" chart?

The "sky cover" represents the percentage of sky obscured by clouds, as would be visible to an observer (or weather instrument) at the Earth's surface.

The most direct reference I could find to this quantity was in this archived presentation from the American Meteorological Society's 2014 Annual Meeting: "The United States Federal Meteorological Handbook (FMH) No. 1 defines sky cover as “the amount of the celestial dome hidden by clouds and/or obscurations”. User @MikeChristianson also identified a direct definition for this exact National Digital Forecast Database (NDFD) product here, confirming this interpretation: "Sky cover is the expected amount of opaque clouds (in percent) covering the sky valid for the indicated hour."

There is also supporting evidence with some additional detail in the NWS Products and Services Reference Guidebook, specifically Section 9, Tables and Abbreviations: The “sky condition” describes the average percentage of the sky that is covered by opaque clouds (not transparent to light) at a given time, followed by a tabular description of the percentile sky cover forecast interpolated to the forecast image in question:

Slight differences in terminology aside, both of these descriptions equivalently refer to the portion of sky obscured by clouds. For typical weather forecasts (as in the example in question), either would be interpreted as describing the sky as it would be visible to an observer at a given point on the Earth's surface - the visible "celestial dome". See also the definition quoted here from the 2nd edition of the American Meteorology Society's glossary, defining cloud cover as "observed from a particular location," in this case the Earth's surface.

Additionally, as noted in the comments, the definitions are vague enough that the observer could be a person or a weather instrument - naturally, the exact details of what would be visible and quantified depend on the observer, but for a simple use of this forecast parameter, the FMH & NDFD definitions support this interpretation of sky cover.


Could we cool the ocean by reflecting away sunlight to influence hurricanes?

Water has a large thermal capacity.

  which is why the temperature change between seasons is gradual rather than sudden, especially near the oceans.

For water to lose heat time is required. By changing the albedo of the ocean just prior to a hurricane/cyclone/typhoon passing over a section of water will not give the water enough time to cool down to have any significant effect.

If it were possible to change the temperature of water to minimize the strength of hurricanes/typhoons/cyclones, large sections of ocean would need to have their albedos changed for a long period of time.


Depth of the wind-induced currents and the shoreline

First, a bit of background, following the Navier-Stokes equations and assuming steady state conditions, we can obtain a simplified balance known as Ekman balance for the boundary layer of the upper ocean (order hundred meters). In this part of the ocean horizontal gradients are small compared to vertical gradients. Under these conditions, there is a balance between Coriolis, and friction (sometimes the pressure gradient force needs to also be considered in the balance).

The resulting theoretical flow has a vertical structure with decreasing magnitude with depth and flow direction progressively rotating to the right (left in the southern hemisphere) as depth is increasing. This is known as the Ekman spiral.
 Source: http://offshoreengineering.com

Under these conditions, in the open ocean (in fact assuming an infinitely deep ocean), the transport in the Ekman layer is perpendicular to the wind direction. The depth of the Ekman layer, or Ekman depth, is simply dependent on Coriolis and viscosity: $$D_e = \sqrt{2K_m\over f}$$ where $K_m$ is the kinematic turbulent viscosity (assumed constant) and $f$ is the Coriolis parameter (a typical value for mid-latitudes is $10^{-4}$ $s^{-1}$). Typical Ekman depths are of order 10 to 30 meters. Typical values for $K_m$ are around $0.1$ $m^2/s$ (not to be confused with molecular viscosity that is around $10^{-6}$ $m^2/s$) but can vary by several orders of magnitude, resulting in a range of $D_e$ of $10-150m$. A good explanation of Ekman balance and much more information can be found in Cushman-Roisin & Beckers (Chapter 8).

While all that is useful in a theoretical setting, the depths obtained from observations of the Ekman depth can be approximated by the expression: $$D_e = 0.4\sqrt{\tau\over f^2 \rho_0 }$$ where $\tau$ is the wind stress magnitude and $\rho_0$ is water density. The factor $0.4$ has been derived empirically and can varied slightly under different oceanic conditions.

All that was describing the surface boundary layer, but in reality a bottom boundary layer also forms. The solution to the bottom layer can also be approximated as an Ekman layer for which the forcing is not the wind, but the bottom friction. The resulting boundary layer also has an Ekman depth approximated with the expression above. In an sloping bottom, the boundary layer follows the bottom slope and the thickness (depth above bottom) of the layer (measured perpendicular to the bottom) is scaled by the cosine of the bottom slope.

Taking 10 meters (one of the smallest values in mid-latitudes) for the surface and bottom boundary layer thicknesses, that implies that in water depths shallower than 20 m the two boundary layers overlap. In water depths shallower than that, the transport in the surface layer is no longer perpendicular to the wind direction. The momentum input into the water by the wind (wind stress) is affected by the presence of the bottom and it is directly dissipated by bottom friction. The mixing by the wind (and wave breaking near the surf zone) results in additional mixing through the water column and facilitating well-mixed water columns. Under these conditions the wind-induced currents extend all the way to the bottom with the direction of the flow being a function of bottom depth, wind direction, and bottom slope. The presence of wind-induced currents does not preclude the occurrence of flow in the opposite direction of the wind.

A fantastic article summarizing the different flow conditions under different wind and wave fields in shallow water depths is given in Lentz and Fewings (2012). (Reprint)

Additional factors to consider are:

stratification by temperature and salinity that reduces mixing and helps maintain a separation between surface and bottom layers (some effects described here).
what happens when the wind is changing speed and direction (non-steady state)?
what happens at the Equator where the Coriolis parameter is zero?


Volcanic lightning was documented by one of the earliest American volcanologists, Frank A. Perret in the 1940s. However, there was not much, if any, study of the phenomena until recently. According to this article, the electric charge develops close to the ground where the plume gas thrust region at a few hundred meters above the crater rim, where the overpressure of the turbulent volcanic jets determines the electrification of particles generating a complex charge structure in the growing plume."

The abstract of another journal article indicates that additional lightning can be generated in the stratosphere, "Observations and volcanic plume modeling further suggest that electrical activity was enhanced both by ice formation in the ash clouds >10 km above sea level and development of a low-level charge layer from ground-hugging currents."


What is the origin of the River Citarum on Java, Indonesia?

The Citarum River,

  originates in Situ Cisanti located at the foot of Mount Wayang, which lies to the south of the city of Bandung, and travels to the north until it empties into the Java Sea.

Situ Cisanti is a artificial lake, about 5 to 10 ha in size, depending on which sources one reads. It is fed by seven springs: Citarum, Cikoleberes, Cikahuripan, Cihaniwung, Cisadane, Cikawudukan, and Cisanti springs.

The picture below shows the designated origin of the Citarum River.

Aerial view of the lake.


A brief, very simply explanation.

The earthquake that struck Nepal on 25 April 2015 occurred in a region with a history of catastrophic tremors.

The Indian tectonic plate is slowly moving at an average rate of 45 mm/year underneath the Eurasian plate. Such movement is never smooth and sometimes the plates get temporarily stuck. Eventually the force producing the movement of the Indian plate increases the pressure on the snagging point, or points, and the plate can jolt. This jolt sends out shock waves through the neighbouring rock which causes the earthquake. The bigger the jolt the stronger the earthquake.

More detail:

From the news services and the US Geological Service

  the USGS wrote in its summary, the quake "occurred as the result of thrust faulting on or near the main frontal thrust between the subducting India plate and the overriding Eurasia plate to the north. At the location of this earthquake, approximately 80 km to the northeast of the Nepalese capital of Katmandu, the India plate is converging with Eurasia at a rate of 45 mm/yr towards the north-northeast, driving the uplift of the Himalayan mountain range. The preliminary location, size and focal mechanism of the April 25 earthquake are consistent with its occurrence on the main subduction thrust interface between the India and Eurasia plates."

  According to the USGS, a quake in the region in 1934 is believed to have killed roughly 10,600 people. Another in 1988 killed 1,500.
  But the experts said big quakes in the area are not necessary common.
  "Although a major plate boundary with a history of large-to-great sized earthquakes, large earthquakes on the Himalayan thrust are rare in the documented historical era," the UGS wrote. "Just four events of magnitude 6 or larger have occurred ... over the past century."


Good Open source alternatives to SeisImager & RES2DINV?

Actually, there are plenty of alternative DC Inversion software;

I hate using res2dinv; unfortunately it's relatively easy to get into. I would have much preferred ZondRes2D for it's user experience.

Look up DC2DInvRes (Resistivity.net), and BERT by Thomas Gunther. You may need to contact Thomas for Bert codes, however it's all done in Python and easily read.

Here is another one, but in matlab. (http://www.degruyter.com/view/j/acgeo.2015.64.issue-2/acgeo-2015-0071/acgeo-2015-0071.xml)

Here in another, again in matlab.
https://github.com/cageo/Karaoulis-2013


Storing borehole interval data (logs) in netcdf

tl;dr

I would suggest considering the borehole measurements as a vertical profile. In terms of the CF Convention it would be a featureType profile, which is described in the discrete sampling geometries section.

I didn't include all reference to the relevant sections in the CF Conventions below. Just comment if import things are missing.

Example netCDF

netcdf borehole_01 {
dimensions:
      z = 8 ;
      profile = 1 ;
      nv = 2 ;

variables:
    int profile(profile) ;
          profile:cf_role = "profile_id";
    double time(profile);
        time:standard_name = "time";
        time:long_name = "time" ;
        time:units = "days since 1970-01-01 00:00:00" ;
    float lon(profile);
        lon:standard_name = "longitude";
        lon:long_name = "longitude" ;
        lon:units = "degrees_east" ;
    float lat(profile);
        lat:standard_name = "latitude";
        lat:long_name = "latitude" ;
        lat:units = "degrees_north" ;
    float z(z) ;
        z:standard_name = "depth";
        z:long_name = "depth below ..." ;
        z:units = "m" ;
        z:positive = "down" ;
        z:axis = "Z" ;
        z:bounds = "z_bnds" ;
    float z_bnds(z, nv) ;
    float temp(profile, z) ;
        temp:standard_name = "soil_temperature" ;
        temp:long_name = "soil_temperature" ;
        temp:units = "K" ;
        temp:coordinates = "time lon lat z" ;
        temp:cell_methods = "z: mean" ;

// global attributes:
        :featureType = "profile";

data:

 z = 5, 15, 25, 45, 55, 70, 90, 150 ;

 z_bnds =
  0, 10,
  10, 20,
  20, 30,
  40, 50,
  50, 60,
  60, 80,
  80, 100,
  100, 200 ;

 temp = 279.4, 278.8, 278.3, 277.0, 277.1, 276.9, 277.0, 279.2 ;
}

Explanation

The variable temp is an example for a data variable in the file.

Basic depth information

Please see CF Conventions section 7.1 Cell Boundaries for details.

z_bnds provides the boundaries of intervals over which the temperature is averaged. The attribute bounds of variable z points to these boundary data. In this example, I wrote the average depth of each interval into the z variable. However, this is no requirement (as far as I know). It might be also the upper or lower depth of each interval or another depth in between. z_bnds automatically inherits all relevant attributs from z. Therefore, it has no own units and standard_name attributes.

As you can see in the data for z_bnds (or in the table below), it is possible to skip depth ranges (between depth invertal 3 and 4) and to have differently spaced intervals (e.g. interval 6 compared to previous intervals).

| **depth interval** | **minimum** | **maximum** |
|--------------------|-------------|-------------|
| 1                  | 0           | 10          |
| 2                  | 10          | 20          |
| 3                  | 20          | *30*        |
| 4                  | *40*        | 50          |
| 5                  | 50          | 60          |
| 6                  | *60*        | *80*        |
| 7                  | 80          | 100         |
| 8                  | 100         | 200         |

Connecting data variables proberly to the vertical coordinate

Please see CF Conventions section 7.3 Cell Methods for details on the first paragraph and section 5.2 Two-Dimensional Latitude, Longitude, Coordinate Variables for details on the second paragraph.

The value z: mean of the attribute cell_methods of variable temp tells us that the temperature is an average value in z-direction over the interval provided in z_bnds. The latter (over the interval provided in z_bnds) is implicitely assumed.

The value time lon lat z of the coordinates attribute of variable temp indicates that the variables time, lon, lat and z provided temporal and spatial dimensions to each value of temp. Really proper software should be able to map each value of temp a the correspondinc time, lat and lon values via the dimension profile. However, implementing this properly is hard work. Therefore, most software package won't recognize it or might even have issues with it. I your software package throws errors, one first step should be to remove the attribute coordinates.

Setting global attributes

Please see CF Conventions section Features and feature types for details

The global attribute featureType is mandatory when we have a discret sampling geometry.

Several boreholes with different depth intervals

For details on coordinate variables and auxiliary coordinate variables please have a look into sections 1.2 Terminology and 5 Coordinate System

Via the dimension profile we could save several boreholes in one file. In the current situation, the samplings depths of each borehole would be the same. One could make the variable z and z_bnds dependent on profile to provide depth intervals for each borehole. However, then we needed to rename the variables z and z_bnds because a variable might only have the same name of a dimension, if the variable is onedimensional and only depends on this dimension (a coordiante variable). When a variable like the depth depends on two dimensions, it becomes and auxiliary coordinate variable and has to have a different names. Thus, we would have double depth(profile, z) instead of z(z) and double depth_bnds(profile, z, nv) instead of z_bnds(z, nv). The temp:coordiantes would have a value of time lat lon depth (because depth is the vertical coordinates) but temp:cell_methods would remain z: mean (because we calculate average in z-direction).


Minerals are defined by chemical composition and crystallography. Dana classification scheme or new Dana classification scheme divides known mineral species in eight broad groups based on primary chemical properties and crystal form.

Organic vs inorganic

Silicates vs non-silicates

  Subdivided silicates into smaller groups by crystalline structures

Metals vs non-metals

  Subdivide non-metals into smaller groups
      metal oxides
      metal sulfides
      metal and non-metal acid salts organized by dominant anion (1)(2)

(1) In chemistry, a salt is an ionic compound that can be formed by the neutralization reaction of an acid and a base.1 Salts are composed of related numbers of cations (positively charged ions) and anions (negative ions) so that the product is electrically neutral (without a net charge). These component ions can be inorganic, such as chloride (Cl−), or organic, such as acetate (CH3CO−2); and can be monatomic, such as fluoride (F−), or polyatomic, such as sulfate (SO2−4).  (from wikipedia)

(2) Some these subgroups represent hypothetical acid-base reactions not actualy seen in nature but modeled after known acid-base reactions. My opinion here is that this approach was used because it was popular theory in chemistry at the time when James Dwight Dana published his work in 1837.

There are other minerals classfication systems

1) By crystal system -triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and cubic.

2) By Cleavage, parting, fracture, and tenacity

3) By mineral hardness

4) By specific gravity or density

5) By mineral association (my favorite and often the best way of identifying and unknown mineral especially when the location is known.


Madden-Julian Oscillation (MJO) - How to interpret the index?

I prefer to use the BOM MJO index and the explanation provided over there -

  When the index is within the centre circle the MJO is considered weak, meaning it is difficult to discern using the RMM methods. Outside of this circle the index is stronger and will usually move in an anti-clockwise direction as the MJO moves from west to east. For convenience, we define 8 different MJO phases in this diagram.

So in your case your signal value is 0.6 and that means it is fairly weak in amplitude as it is inside the circle. You also need to mention the phase of the MJO phase diagram. There are eight phases

Phase 1 & 8 - Western Hemisphere And Africa

Phase 2 & 3 - Indian Ocean

Phase 4 & 5 - Maritime Continent

Phase 6 & 7 - Western Pacific.

Currently the signal is a weak one as seen in this phase diagram MJO Phase diagram

When the MJO signal is strong it's amplitude will be greater than 1 and the contour line will be outside the circle. It should be noted that the MJO is an empircal index consisting of the 850 hPa winds, OLR and 200 hPa winds.

MJO passage through phase 6 and 7 is always of global interest as the impact can be of planetary scale. Usually El Ninos are preceded by Westerly Wind Bursts and the forcing factor can be a MJO passage through phase 6 and 7.

One can look at the raw data of the signal here - RMM Index text. This provides the amplitude of the signal as well as the phase of the MJO.

Another version of the same can be seen here - MJO RMM index


Employment of Volcanologists and Petrologists

The first department that comes to mind is obviously academia, i.e. faculty or research positions at universities. However, there are many other career paths available (hopefully: there are not enough academic positions for every student completing a Ph.D...). For volcanologists, there is actually a nice site called volcanologists outside academia. As the name suggests, it compiles various testimonies about people who got a Ph.D. in volcanology but now work outside academia. They have also conducted a survey to gather more data. The results show that there are very diverse employment possibilities, including (but not restricted to):

Outreach/science communication
Museums
Academic publishing
Data analysis
Material science
Instrument companies
Tourism...

I'm not sure for igneous petrologists/geochemists, but I think the possibilities are pretty close.
Edit: I just realized I completely forgot to mention volcano observatories, which represent a "large" source of employment for the community (relative to its size). IAVCEI's Early Career Network has put up a series of videos where observatory workers describe their job. Another series about careers in academia is planned, so stay tuned!


Grid Resolution of the NCEP/NCAR reanalysis data

The data is available on different grids depending on the variables of interest:

The original grid is a T62 Gaussian grid, which grid cells do not have a constant lon-lat spacing over the whole globe (see e.g. Gaussian grid at Wikipedia), with 28 sigma levels. The sigma levels are listed on this page, where also the surface pressure is available for download. According to the FAQ, the sigma coordinate equals "pressure in current level"/"surface pressure". Thus, the pressure on each sigma level can be obtained via the available data.
Secondly, some variables, such as air temperature, are available interpolated onto an equally spaced lon-lat grid of 2.5° x 2.5° resolution with 17 pressure levels. These data are available here including data files with the geopotential height. The pressure levels are listed on the same page.

Comparability with A and B coefficients of ECMWF

The ECMWF uses the coefficients A and B as well as the reference pressure $p_0$ to describe the levels. NCEP uses just one coefficient ($\sigma$) for this purpose:

$$
\begin{align}
& \text{ECMWF:} & p\left(\eta,p_s\right) &= A\left(\eta\right) \times p_0 + B\left(\eta\right) \times p_s \\
& \text{NCEP:} & p\left(\eta,p_s\right) &= \sigma\left(\eta\right) \times p_0 \\
\end{align}
$$

When we had $p_0 = 0\ \text{bar}$, both representations were the same. In the given situation, there is no unambiguous mapping from a known $\sigma\left(\eta\right)$ to a set of $A\left(\eta\right)$ and $B\left(\eta\right)$. At least if there are no further limiting conditions.

Normally, I do not deal with vertical coordinate systems. Therefore, I am not sure how data on different vertical layers is compared with each other (except of the obvious: interpolating the one data set onto the vertical coordinates of the other data set).

Summary

1) Yes, here and here.

The variables Air Temperature, Geopotential Height, Relative Humidity, Specific Humidity, Omega (Vertical Velocity), U-Wind, and V-Wind are available on the second grid (2.5° x 2.5° horizontal resolution; 17 pressure levels).
The variables Divergence, Specific Humidity, Virtual Air Temperature, and Relative Vorticity are available on the first grid (T62 Gaussian grid; 28 sigma layers). In order to calculate the model pressure at each sigma level, the surface pressure is additionally provided (T62 Gaussian grid but no vertical resolution). Also the topographic data (Orography) is provided on the same resolution as the surface pressure.

2) The vertical layer representations by $\sigma$ coordinates, on the on side, and by a set of $A$, $B$ and $p_0$, on the other side, are similar to each other but not directly comparable to each other. However, for most practical applications the data on the second grid, which is easier to handle, might be sufficient.

I am not sure if these answers to the question are satisfactory.


At least by theoretical biology - could there be, or should there have been, or was a "southern Taiga" on earth?

I'll define a boreal forest as a place that

Has trees, i.e., long-lived woody plants that are capable of growing at least ten meters tall and that grow both upward by extending new branches and outward by widening of the trunk. Amongst other things, this rules out times before ~380 million years ago, which was when the first trees formed.
Has sufficient trees so as to constitute a forest, which I'll define as a largish area where trees grow sufficiently dense so as to form a more or less closed canopy. This distinguishes forests from areas with only  a few trees such as savannas and krummholz.
Has very harsh winters, with at least one month where the average temperature is well below freezing, and temperatures of -40° C are not rare. This distinguishes boreal forests from cold oceanic forests such as the Magellanic subpolar forests in southern Chile and Argentina.
Has mild summers, with only a few months where the average temperature exceeds 10° C. This distinguishes boreal forests from hemiboreal and temperate forests. Note that some scientists do not make this distinction, classifying Köppen climate zone Dfb as boreal.
Is extensive. This distinguishes large boreal forests from high altitude subalpine forests that would locally pass the above tests. Subalpine forests can occur at any latitude, including Australia's Snow Mountains, New Zealand's Southern Alps, and parts of the Andes.  This is not a clear-cut boundary. As a climate cools, subalpine forests may spread to the valleys between mountains and then spread out beyond the mountains. At some point, such montane forests becomes boreal forests.

The need for very harsh winters coupled with the need for extensiveness means that boreal forests did not exist between the start of the Triassic ~252 million years ago to the end of the Eocene ~34 million years ago. The Earth was in its greenhouse phase during that long stretch of time, with balmy temperatures year round even at the poles. A boreal forest can only exist when the Earth is in its icehouse phase. This includes the last 34 million years, and also the period between 340 to 250, the late Paleozoic glaciation. Unlike the current ice age, the Earth in the late Paleozoic had land (Gondwana) surrounding the South Pole.

The question then is whether either of those intervals (the last ~34 million years, and the late Paleozoic glaciation) have supported a southern boreal forest. Per Rees-Owens et al., the answer is a possible yes to the more recent interval. Forests existed on Antarctica until as recently as 2.5 million years ago. Whether these were Köppen climate zone Cfc (subpolar) or Dfc (continental boreal) is very hard to say. If one relaxes the requirement for cold winters a bit, then those recent forests almost certainly were boreal.

Per Horton, et al., and also per the authors of several related papers, the answer is an unequivocal yes to the more ancient interval. Moreover, the boreal forest of that ancient time would have been very much like the boreal forest of present time: One of the largest ecozones on the planet.

References:

Horton, D.E., Poulsen, C.J. and Pollard, D., 2010. Influence of high-latitude vegetation feedbacks on late Palaeozoic glacial cycles. Nature Geoscience, 3(8), p.572.

Pross, J., Contreras, L., Bijl, P.K., Greenwood, D.R., Bohaty, S.M., Schouten, S., Bendle, J.A., Röhl, U., Tauxe, L., Raine, J.I. and Huck, C.E., 2012. Persistent near-tropical warmth on the Antarctic continent during the early Eocene epoch. Nature, 488(7409), p.73.

Rees-Owen, R.L., Gill, F.L., Newton, R.J., Ivanović, R.F., Francis, J.E., Riding, J.B., Vane, C.H. and dos Santos, R.A.L., 2018. The last forests on Antarctica: Reconstructing flora and temperature from the Neogene Sirius Group, Transantarctic Mountains. Organic Geochemistry, 118, pp.4-14.


Seeming correlation between time of day and earthquakes

As noted by Barry Carter, you could take a look at https://www.emsc-csem.org/Earthquake/Map/zoom.php?key=75&typ=NAM#5 to see the earthquakes around San Francisco, with a table below giving the time and date of the earthquake(s). The USGS can create a similar data-set (try there). I used that to extract the earthquakes of magnitude 2.5+ in the SF area over the last 4 years:

Or the same data with a Kernel Density Estimator in a polar plot (the circle corresponds to 24 hours):

Based on these plots, I think that there is no specific trend suggesting that earthquakes are triggered at specific times.

Earthquakes tend to have their 'core' at depths of a few km's usually, which means that they are not very aware of day-night cycles. Apart from perhaps very minor influence of gravitational differences due to sun/moon locations (which vary over the course of a month), the earthquakes truly follow their own 'random schedule' with respect to our life-cycle!


Will carbon tax be applicable to other emissions?

The recent Canadian carbon tax law is just CO2.

CO, which you asked about, is bad for people and considered a pollutant.   Efforts to reduce CO have been underway for decades including carbon monoxide detectors in homes and garages and catalytic converters in cars.  The target behind CO is elimination as much as possible.   Being a pollutant, there's a different approach.  The tax is only meant to discourage CO2 production, which isn't a pollutant, but it is a problem due to gradual build up.

Likewise, sulfur pollution, coal being a primary source, is dealt with by strict limits (in the US), and scrubbers which catch sulfur emissions have been installed in coal plants since the 1980s.

Additional greenhouse gas taxes like CH4 might eventually come to pass, but, like CO, the effort with the greehouse gas CH4 should be elimination, not taxation.  It's also harder to put a number on CH4 released, which can be released by fracking seepage or cow burps primarily or by escape from thawing tundra (are we going to tax teh ground?).    CO2 is easier, for example because we know how much CO2 is produced per gallon of gasoline, for example, so a tax is straight forward, but cow burps or fracking seepage could be estimated so a tax there might be possible.

But the general idea, is that, if it's preventable and a pollutant of some form, then regulation should aspire to reduce output, even strive towards elimination.   CO2 is an inevitable byproduct of burning fossil fuels, so it can't be eliminated and it would be problematic to try, but it can be financially discouraged by taxation.


I'm a bit confused why you say the water particularly comes from Afghanistan and India.
Perhaps the colors on that graphic end up being a bit confusing...

As your own graphic does indicate, the Indus basin basically runs the spline of Pakistan.  One could argue many many other countries aren't nearly as lucky, with rivers quickly cutting across their country, and so are under much more dependence on countries upstream (and owe more responsibility to countries downstream) (a random example).

Almost all of the water from the northern Pakistani mountains does flow down into central and southern Pakistan and to the Indian Ocean.

Perhaps this fact will be more apparent if the outlines of all the major waterways flowing eventually down into the Indus are shown (from RiversNetwork.org):

All that area of blue waterways is flowing into the Indus, and down through the country.  These rivers include large gatherings into the Chenab, Sutlej, Zaskar, Nubra, and Shyok, all of which spill into the Indus.  Yes it does include some parts of northern India/Kashmir and eastern Afghanistan.  But note how it also covers almost all of the land in northern Pakistan.

For good measure, this second image, put together from the wealth of tools at worldmap.harvard.edu, hints that most water in SE Pakistan also flows into the Indus (the red outline is all areas from which water will flow into the Indus) (Why a discrepancy?  Only guess is maybe SE Pakistan isn't shown on the other map because there's not major waterways in that area?!?!?):

Then when you add in the other independent rivers basins in Pakistan like the Dasht, Nal, and Hub:

(Source: Rivers of Pakistan on Wikipedia)

you can see most of the rivers/water out west also stay within the country rather than leaving it.

So in the end, almost every drop of rain that falls on Pakistan, from the northern mountains, to the deserts, to the heartland, to the Iranian border, all of it already flows further on into Pakistan for use.  In the end, there's only a few tiny slivers of land in the country where water falling on the country is lost by flow to other countries (such as places like near Chaman).

What may be confusing about the image you gave is the gray colors up in the north, nearer the Basha [as well as in southwestern Pakistan].  But those colors aren't showing that water there isn't coming into Pakistan, but only that that area isn't irrigated land.  Basically all the water in those gray areas, like the Pakistani portions of the Hindu-Kush/Himalayas/Karakoam Mountains, flows down into the Indus and through Pakistan.  Plus even more additional water from neighboring countries.  So in other words Pakistan gets more water than what falls on it.  Other countries definitely aren't so lucky.  And that water is already coming down into the rest of the country.

In terms of feeding the country, irrigation could maybe be expanded a bit... but I believe the problem isn't just the lack of water... but that a lot of the rest of the soil is fairly poor for growing.  The book Pakistan's Soil Resources (available online) classifies more than 1/4 of the country's soil potential as "Agriculturally unproductive or non-agricultural land" due to wind erosion, salinity, extremely low, etc.
(Some additional land might be made arable by leeching down salt levels, but I believe it would take long periods of time and great amounts of water.  Soil Quality and Agricultural Sustainability, by Rattan Lal, (Google Books preview) looks to include some interesting description of the greater complexities and some longterm potentials of many areas of the world, including Pakistan.)

On the other hand, it sounds like the more urgent concern maybe isn't eating but drinking? (disappointingly, I think you'll find that most other parts of the world probably aren't very aware of your problem... I certainly wasn't).
 Unfortunately this problem trend is showing up in many other places, such as well-publicized recent issues in southwestern South Africa.

In the end, the primary problem for much of Pakistan's population likely doesn't stem from the complete lack of water... but instead from the lack of needed infrastructure, pollution issues, and mismanagement, as this article alludes to.  These are issues that the entire world does struggle with (such as the troubles in Flint in the USA).  There are already some parts of the world there are literally growing concerns of not enough water existing in entire regions to sustain it whatsoever (such as in the SW US/impacting NW Mexico)  But if the Indus is flowing steadily down to the ocean through your country (please do indicate if its not, the problem may be more serious then!!), it's not a matter of no water, it's the challenge of detouring enough to areas to meet needs, and keeping it clean.

Karachi does look a bit of a challenge area on a map.  The good news is it's only 60 miles from the Indus.  Quite a few places source their water from such a distance - some places even do get it from hundreds of miles away, even from the far side of mountain ranges as you kind of suggested.  But the unique challenge is likely how flat the land is in the south nearer Karachi.  Places like California and Rome can take advantage of elevation to better feed the distant water via gravity.  Karachi on the other hand may require much more pumping to get the water from the Indus, and that might well not be cheap/easy over even distances like 50 miles?

(As noted, It looks like most of the N Pakistani mountains are south of the continental divide, so almost all their water flows your way already. The few miles of snowpack flowing into Tajikistan and such a) may well be needed by their people and b) could be diverted into the Indus with some effort... but that won't solve problems if the Indus is already steadily flowing. The Suleiman Range might offer some options to divert a little more water west of the Indus using tunnels like in the LA Aqueduct... but that would depend a great deal on the specifics of the topography and precipitation levels in those mountains for feasibility.)

So, indeed, all that water from your mountains is indeed flowing right through the heart of Pakistan, "right by your window" almost (plus the gift of extra input from areas of far northern India).  It's just getting it from the Indus to where it's needed.  And it just needs to be taken care of carefully.

In the end, Pakistan actually has it a lot better water-wise than much of the Arab world.  Find the green:

(image from NASA MODIS satellite)
That spline is the blessing that led to the growth of your country... and its the great challenge of the country today.


What is the geologic origin of Cryolite, and could there be more?

Ivigtut Cryolite deposit, Ivittuut (Ivigtut), Arsuk Fjord, Sermersooq, Greenland deposit  is the first and largest occurrence of Cryolite but it is not the only location to report Cryolite.

Some of these other locations listed below have produced collectible specimens but cryolite does not occur in large enough quantities to be mined.

Other important locations are: Reference
(I have seen specimens from these locations)

ST PETERS DOME PEGMATITE PROSPECT, Pikes Peak Colorado
Francon Quarry Mt St Hilaire, QUEBEC

(other listed locations, Reference)

MONZONITE GROUP, Gunnison County Colorado
FANNY GOUGE MINE, Yancey Co. North Carolina
BARINGER HILL MINE, Llano, Texas
MC GUIRE PIT Marathon Wisconsin.
Also reported from near Miass, Russia.

I also suspect there are other Canadian locations but Canada publishes less mineral data than the United States does.

Cryolite occurs in syenite vein deposits (igneous rock which is quartz deficient) which had abundant amounts of fluorine.

There are 13 other known mineral specimens that contain primarily: Na, Al, F.
Cryolite is the most common mineral, due entirely to the Ivigtut deposit.
The geologic conditions at the Ivigtut deposit led to deposition of cryolite instead of an other mineral phase containing  Na, Al, and F.

I believe additional deposits with cryolite may be found in the future but the Greenland deposit is likely to remain the first and most important deposit.
There are a handful of completely unique ore deposits on earth, Ivigtut Cryolite deposit is one of them.

See zincite from Franklin New Jersey as another example where a specific mineral was only ever commerically mined from a singular location.


How significant is human influence on seismic activity?

You have an interesting graph there. In response to your question, I would say that if we actually use the Mw (moment magnitude) measure of seismic activity (which is related to energy release via slip on a fault), we would see that most of the Earthquakes 'caused' by humans (and note that there are still only a few examples of this, (such as the 5.7 Prague, Oklahoma Earthquake), are limited to intraplate earthquakes which only exceed 4 in rare instances such as the above.

The exact 'percent' of energy release due to induced (human-caused) seismicity would probably not even approach single digits, but possibly reside in values approaching zero, when you consider that the vast majority of earthquakes take place at plate boundaries and outnumber induced earthquakes by a large amount.


What factors determine the height of the turbopause?

A major consideration for any such altitude measurement, hence determination of the factors is, according to Turbopause determination, climatology, and climatic trends using
medium frequency radars at 52°N and 70°N (Hall et al. 2008), that

  measurement of the turbopause altitude is a rather different matter, primarily because direct measurements of small-scale fluctuations in neutral density, temperature or motion at altitudes of around 100km are virtually impossible.

The authors state that seasonal variation plays a significant role; the article The “wave turbopause” (Offerman et al. 2007) elaborate on this in their abstract:

  Substantial seasonal and latitudinal variations are found, with some interannual variability also present. Seasonal changes are annual at high latitudes, semi-annual at low latitudes, and a mixture of both at middle latitudes. Southern hemisphere data are similar as in the North if shifted by half a year. Latitudinal variations show a minimum in the tropics and two relative maxima at middle latitudes.

With observations made in northern Norway in the article Seasonal variation of the turbopause' One year of turbulence
investigation at 69°N by the joint University of Tromso/University of Saskatchewan MF radar (Hall et al. 1998) showing that the levels at these latitudes decreasing in summer months.

However, long term changes seem to be occurring at high latitudes, with the Hall et al. (2008) observing

  the classical radar turbopause has been at constant altitude at 52°N during the last decade. At 70°N, on the other hand it appears to be moving upward at a rate of between 2 and 7 km decade$^{−1}$.

Although the authors suggest that there may be an anthropogenic cause, the observed reasons for the high latitude change appears to be due to:

  the trend we report at 70°N is commensurable with an observed cooling of the mesosphere (and in turn a lowering of the ionospheric E region altitude) and an increasing average electron density in the ionospheric D-region.


What is the physical meaning of the von Kármán constant?

K is in fact just an empirical proportionality value without any significant meaning. In fact each of the tracers (such as temperature, humidity and ...) should have had their own K BUT we dump that coefficient into the (star)* version of that tracer, for example T* (near surface temperature) or Hum* (near surface humidity), so we can still use K in their respective formulation.

Monin-Obukhov length has K in it because that length is related to u*. That length comes from dividing the Shear production and buoyancy procution/dissipation term in turbulence kinetic energy (TKE) equation. As you know Shear production is a function of momentum transfer and u*. Since there is a u* there is a k in there too.

Unlike other stuff in turbulence, the story of K is unfortunately boring.


How likely is that next big earthquake (and tsunami) will hit Philippines again?

How likely is that the next big earthquake (and tsunami) will hit the Philippines again?

The likelihood is 100%, but we don't know when. It could be tomorrow, it could be in a year, or it could be in 100 years. Make sure you are prepared for such an event.

The Philippines are situated above a subduction zone (the Philippine trench), so an earthquake is inevitable. However, it is impossible to predict exactly when and where it will happen.

As for tsunamis, these propagate from earthquake epicentres, so the Philippines are susceptible to tsunamis generated elsewhere in the area.

You are drawing a line between the Bali and Palu events, and suggesting that the next  one will be along that line. This is not how it works. The earthquake could be on the line (by chance), or it could not. There is no way of predicting.


There are some well established means of detection of an imminent volcanic eruption; however, having said that, I must mention the caveat that not all the signs occur and in some cases, such as the recent Mt. Ontake eruption in Japan - volcanoes can erupt without warning.

Another thing to consider is that the notion of a volcanic eruption anywhere on the Australian mainland is enough to get the media all excited and to latch on any piece of sensationalism, as suggested by one of the commenters in this volcanism blog entry, stating in regards to quotes and information the media attribute to Professor Bernie Joyce of Melbourne University (A person I have met in the past and I can vouch for his non-alarmism):

  This nonsense runs in the media here about once a year. What Dr Joyce is really trying to say is that Southwest Victoria has a history of monogenetic volcanism, and should have some contingency plans in place similar to Auckland.

So, the signs of an impending volcanic eruption may include the following (in summary from the Oregon State University page What are the signs that a volcano is about to erupt?:

First and foremost, increased or anomalous seismicity (as suggested by Gary Kindel in the comments to the question).  Earthquakes are not uncommon in Australia, and not uncommon in the area in the volcanic field of Victoria and South Australia.  Some earthquakes have been reasonably severe, as shown in the map below, from the University of Western Australia's webpage Volcanoes & Earthquakes in SE Australia:

The red triangles are a handful of the more 'significant' of the 300+ volcanoes in the region.  The purple represent earthquakes with magnitudes greater than 4.0 on the Richter Scale and the blue are all earthquakes recorded between 1995-2004.  A word of caution with this map, it has been only since the 1960's that earthquakes have been recorded.  The largest earthquake in the region was 6.5 on the Richter Scale in 1897 (north-west of Mt. Gambier).

Additionally, as earthquakes in Australia are intraplate, they tend to be considerably shallower than those from plate margins, with, according to the Seismology Research Centre, depths usually less than 20km.

According to the USGS earthquake track page for Mt. Gambier, there has been some seismic activity in the past 25 years, but not much at all - a total of 5 earthquakes, ranging in magnitude from 2.8 to 4.7 and at depths from 10-22km - not particularly anomalous, especially when compared by the literally dozens of quakes occurring in the state of Victoria (including non-volcanic areas) of similar magnitude and depths in 2014 alone.

However, as more seismographs have been established over the area, there have been some anomalies, including (from the University of Western Australia):

  Three earthquakes of magnitude approximately 2.5 occurred in the vicinity of Mount Gambier in January and February of 2003, with greater than usual depths ( ie, approximately 40 km deep). Some scientists have suggested that they may be generically related to the volcanoes in the area.

According to the Geoscience Australia report Australian Seismological
Report 2008 - addendum, there is still significant seismic activity around Mt. Gambier and comparatively less in western Victoria (as seen in the map above).

The Oregon State University document also states that the presence of fumaroles and ground deformation are also signs of potential volcanic activity.  In terms of western Victoria and south-east South Australia, a conclusion regarding this is given by the University of Western Australia:

  Many of the volcanoes have preserved their classical volcanic shapes, and the region is considered by some to be volcanically dormant rather than extinct. However, note the conclusion by Cliff Ollier ( in "Landform Studies from Australia & New Guinea, by Jennings & Mabbutt, 1967, p 338) - " Perhaps the most remarkable feature of Victorian vulcanicity is its complete cessation. Older basalts were erupted in Victoria through long periods in the Tertiary: the newer basalts were erupted throughout the Pleistocene and extended into the Recent. The last eruption probably took place only 5000 years ago, and yet there are no fumaroles, no hot springs, no anomalous temperature gradients or seismic effects, in fact nothing to indicate how recently the volcanic activity ceased."

However, according to the Romsey Australia blog-page Is there a risk of a volcanic eruption in Australia?, Professor Bernie Joyce has developed a volcanic hazard map, regarding the potential risk according to his research and observations:

And notes that with an eruption that formed Mt. Gambier

  an eruption could either be an explosion like the one at Mt Gambier, creating a big hole in the ground

with little or no warning.  But another scientist Dr. Wally Johnson disagrees in part, stating:

  "If Mt Gambier did erupt it would impact on local communities," Johnson said. "But any changes to the state of these volcanoes would be noticed early on, either through earthquakes, or in the case of Mt Gambier an increase in the temperature of the water.

This leads to one final point, recent studies and observation reported in the Romsey blog section Geologists suspect an earthquake that originated 50 kilometres from King Island in February 2002 signalled the reawakening of the hot spot - Bass Strait is the waterway separating Victoria and Tasmania - this is known as the East Australian Hotspot which is a considerable distance from Mt Gambier and the other Victorian/South Australian volcanic centres.  Shown on the map below:

Image Source:

  Rapid change in drift of the Australian plate records collision with Ontong Java plateau
  Kurt M. Knesel, Benjamin E. Cohen, Paulo M. Vasconcelos & David S. Thiede
  Nature 454, 754-757(7 August 2008)
  doi:10.1038/nature07138


How does composition change if you extend an Earthlike atmosphere 50km below sea level?

This seems like a World Building question. I presume that on your imaginary planet something, perhaps an asteroid strike, has created a huge bowl 50 km deep. On the rim of the bowl, the atmospheric pressure and composition are the same as at sea level on Earth. You want to know what conditions would be like at the bottom.

In some respects a bit like Venus. Very hot, but exactly how hot would depend on whereabouts on the planet this deep bowl had formed. The composition of the atmosphere would be the same as at sea level (I assume the lip of the bowl is not close to a sea). The pressure at the bottom would be many atmospheres, but not as many as the 90 atmospheres on Venus. It would seldom rain down there because of the heat and high pressure. Rain falling in from high altitude would usually evaporate before reaching the bottom. As explorers ascended the sides, conditions would become more normal

Would it be habitable? Yes, but how habitable depends on where on the planet this bowl occurred. There would be habitats on the slopes, where conditions were less extreme than at the bottom. If this bowl or basin were in the tropics, the bottom would probably be too hot to sustain life. A water supply would also be problematic. Pressure is less of a problem; there are life forms 12 km down or deeper in Earth's ocean trenches.


Longitudes and latitudes are not regular in WRF-out files

I am not sure about your R code or your snippet of data.  However, you could do an ncdump on the file and direct the output to text to get it "from the horses mouth" so to speak.

In general, the lat/long export of the WRF grid will not have a linear pattern.  WRF uses a spatially projected grid, likely an equal area lambert method or something similar. The curvature of the Earth creates non-linear grid values in geographic coordinates, but the projected grid coordinates (which you can also export) are linear. See the example WRF domain below plotted in geographic coordinates.

image credit: Zhongfeng Xu and Zong-Liang Yang


To stop anthropogenic climate change, is it necessary to stop extracting petroleum and start pumping carbon under ground?

Summary
Most of the remaining stocks of hydrocarbons (coal, natural gas, oil) will have to remain unburnt. In almost all cases, that will mean leaving them in the ground. We already have proven technology to prevent new emissions of anthropogenic greenhouse gas emissions, and in most cases, the technical and economic barriers are solved: the remaining problem is purely political.
Let's make sure we're talking about the same thing
Let's get our terminology straight first. The big issue is rapid climate change; and we can do something about that, because the current rapid climate change we are starting to experience, is anthropogenic - it is human-caused.  So, "climate change" here refers not to historic natural processes over thousands to millions of years, but the present anthropocentric-induced processes that are happening over decades. That is the most common usage of the phrase these days, but it still can be useful to state it explicitly once in a while so that we know we are discussing the same thing.
Stocks and flows
Climate change is a result of changes in the stock of greenhouse gases in the atmosphere.
Those stocks have increased a lot over the last 180 years or so, and are continuing to change, because we've messed about with the flows. In particular, we've increased the rate of emissions from sources, without increasing the rate of take-up of sinks.
The bathtub analogy: we've opened the bath taps further, so water's flowing in faster. But we haven't widened the drain, so the level of water in the bath is rising, and soon will overflow and flood the house. It's not the rate at which water enters the bath that's the problem in and of itself: it's the level of water in the bath, and that's rising quickly because the inflow is much faster than the outflow. Similarly, what matters for climate and climate change is the level of greenhouse gases in the atmosphere, and the level of CO2 absorbed into the oceans.
Even if new net emissions went to zero this year (technically impossible without the collapse of human civilisation, which would rather defeat the whole exercise), we'd still see increasing heat content of the Earth over the next few years, as things move towards equilibrium, based on the current levels of greenhouse gases which represent a historic increase over the levels that prevailed over the last few thousand years during which human civilisation has developed.
So, to prevent further climate change, we have to balance out sources and sinks. So we can do either, or a combination, of two things: we can decrease the rate of emissions from sources down to the level where existing sinks can absorb them; and we can increase the rate at which sinks absorb.
Reducing sources
Stopping burning fossil fuels is the easiest and simplest way to decrease the rate of emissions. To the best of my knowledge, we now have alternatives for almost all of the major emissions sources, with the possible exception of methane emissions from livestock farming. Some are very well-established, like hydro and onshore wind electricity generation. Some are maturing quickly, like PV, offshore wind. Some are successful in the lab, and ready to move to commercial-scale prototypes, such as decarbonised production of steel, clinker, and liquid hydrocarbons.
Increasing sinks
Carbon sequestration is one way to increase the sinks. It's been trialled at small scale, and remains problematic. The way that's been done so far is to pump the carbon dioxide underground. At the moment, no business has been willing to accept the open-ended liability that is posed by the risk of leakage. So at the moment, the market is saying that this is not a viable technology. That might change. It's also worth noting that at the moment, the market also wants to use CO2 sequestration as a means of extracting even more oil (Enhanced Oil Recovery, EOR), which just makes the whole problem worse.
One way that is fast, effective and affordable is increasing the amount of carbon in biomass. Reforestation and afforestation are two ways to do this, and would help reverse a lot of the human-caused loss of forest ecosystems, if done well.


Does the amount of energy released from burning of fossil fuels have a measurable impact on global warming?

Yes, it does add to global warming.

No, it's not currently measurable.

Human primary non-renewable energy consumption is about 15TW - and pretty much all of that goes into low-grade heat in the ocean surface and the atmosphere.

Expressed in the same terms as the forcing units of global warming, the forcing effect of that heat is about 1.7% of the effect of the anthropogenic release of $\ce{CO2}$ to date.

So we can calculate it, but it's not directly measurable as a change to global energy content: it matters, but it's not quantifiable from temperature observations, given current data and techniques.

(Kudos to Pont for the link to the paper)


It's a huge field of research and a lot of interesting studies have been published over the years, but also a lot of speculation. Models and theories are mostly based on surface structures observed by remote sensing. Potential field data, as gravimetry can also add good constraints when available.

Tectonic forces might be rather common in large young planets, but as they cool the process usually stops. In our time and solar system, plate tectonics as we would recognize it, is only known on Earth (and maybe Titan). Other tectonic forces can cause similar features by local uplifts, volcanism and cryovolcanism.

Venus appears to have some kind of tectonic forces, at least in the past. An important part of the present research focus on the dynamics and chemistry of the Venusian mantle and how the topography helps us understand the development of the planet. The data from Magellan in the early 90's are particularly important. Studies of Venus also helps us to understand how tectonics developed on Earth. It's been suggested that lack of water as a lubricant in the Venusian upper mantle makes continental drift impossible, but mantle dynamics can still cause local/regional uplift and volcanism.

Europa is also supposed to have an odd kind stress that resembles tectonics, but caused by the tidal force from Jupiter that deforms the moon. We have the same tidal deformation on Earth, but it is much stronger on Europa and can cause dynamics that mix the interior. Somehow similar to how one should roll and press a lemon before cutting it to squeeze out the juice. Galileo is the main source of data, still.

Titan could be the candidate that most resemble Earth, but it's under debate and more data is needed.

The reasons to have plate tectonic on a planet are complicated and debated. I wouldn't say that we even have a good understanding of the continental drift on Earth yet. The best approach to understand what is needed for tectonism is to look at the planets energy budget, how much heat is generated and how can it leave the planets internal system. The chemistry of the mantel is also important, some dishes boil, other dishes fry. Water or other fluids appears to be crucial.

A rather interesting suggestion for Earth's unique plate tectonics is that it depends on additional energy harvested by the photosynthesis in green plants. It might be that life is the cause of mountains instead of the other way around.


Why is the weather in San Francisco so different from that in San Jose?

San Francisco is more exposed the maritime westerly weather, whereas San Jose is in a rain shadow region with a mountain range between it and the Pacific Ocean.

San Jose, California, and adjacent cities are usually drier than the rest of the San Francisco Bay Area because of the rain shadow cast by the highest part of the Santa Cruz Mountains.

The exposure of San Francisco to westerly maritime weather and being surrounded by water on three sides are significant reasons why it has a milder climate. It's climate is largely influenced by the cold California Current.

Due to the Bay Area's diverse topography, the result of the clashing tectonic plates, the region is home to numerous microclimates that lead to pronounced differences in climate and temperature over short distances.

San Jose however is slightly inland and is surround by mountains of three sides, has less rain and an average of 301 sunshine days each year.


Best book/s of oceanic data treatment and analysis?

My personal best is the Emery & Thomson "Data Analysis in Physical Oceanography". You can find some used versions of the previous editions for a much cheaper price. If you are able to manage the methods described in that book, you will be in good shape. The only one of your requirements it doesn't do is the coding part. If I had to choose one book, I would definitely buy this one and then search for code elsewhere.

If you want a book that provides some code and explains some of the analysis, you could try the Malek-Madani "Physical Oceanography: A Mathematical Introduction with Matlab". It is theoretical rather than focus on data analysis.

A relatively cheap book with some useful codes, not specific to Oceanography is Middleton "Data Analysis in the Earth Sciences using Matlab". Some of it might be slightly outdated, but still useful.


wavelength of seismic wave with a gaussian source

The Fourier transform of a Gaussian is a Gaussian.

If your signal is given by

$$g(t)=\frac{1}{\sqrt{2 \pi \sigma^2}} \exp\left( -\frac{(t-t_0)^2}{2 \sigma^2}\right)$$

then your frequency spectrum is

$$G(f) = \exp(-2 \pi^2 \sigma^2 f^2),$$

where f is the frequency ($2 \pi \omega).$

I'l let you do the substitution with your particular numbers. Enjoy!

https://warwick.ac.uk/fac/sci/mathsys/courses/msc/ma934/resources/notes8.pdf
http://www.cse.yorku.ca/~kosta/CompVis_Notes/fourier_transform_Gaussian.pdf


Definition of "hundreds of ice accretion"

I think that it's referring to hundredths of an inch, and "hundreds" is a typo for "hundredths". This FAA information sheet says:

  All ice accretion amounts will be reported to the nearest one-hundredth of an inch (0.01 in.). An example of a 1, 3, 6-hour ice accretion remark would be: “I1010 I3015 I6022.” This translates to 0.10 inches of ice in the last hour, 0.15 inches of ice in the last 3 hours, and 0.22 inches of ice in the last 6 hours.

This also matches the terminology used in some other weather reports, such as this one:

  Ice accretion was light, generally only a few hundredths of an inch...


What creates so much pollution in Africa?

What you can see in your image is mostly dust from the shara desert https://earth.nullschool.net/#current/particulates/surface/level/overlay=pm10/orthographic=13.33,5.70,766

You will be able to see other types of particles like smoke from combustion like cars-industry-forrest fires,the picture show particles that is 10 microns in size.

On the linked page you can find details of other types of chemical and particulate emissions too like SO2 and CO2 it is a good source for information about the atmosphere.


Can anyone explain this frost phenomenon?

I am going to take a guess and say these are Frost crystals. There are few conditions that need to be present in order for these to develop

1) Light wind

2) Relatively high humidity(some water vapor source perhaps nearby)

3) Depending on your elevation - was this somewhere near a mountain basin ?

As opposed to snow crystals frost crystals form more near the surface

You can read this article  Frost crystal and if the answers are in the affirmative
then you know these are the conditions that caused these crystals to form.


How to calculate air temperature for given time, location and altitude?

The answer that you seek does not exist. There is no easy formula that might be useful. Deriving a temperature based on solar radiation is also unrealistic since the system has very large latencies and complex interactions so that you'd end up writing a full-blown weather model. This appears to be beyond the scope of your intentions.

The question you need to ask is which effects are relevant to you and how accurate you want your model to be. Also, which quantities do you require? The answers affect your course of action. Since you mention flying aircraft, you may likely want vertical profiles of atmospheric temperature above a given location. Also, which of the following effects do you need to include:

Day/Night variation
Clouds, rain
Temperature inversions
Effect of different terrains (ocean vs land for example)
Large variations in geographical location
possibly other things

A good starting point are standard atmospheres (c.f. https://en.wikipedia.org/wiki/International_Standard_Atmosphere). Another approach would be to find real-world locations that are sufficiently similar to the locations you are interested in and provide measurements. These might be adapted to your situation.

A simple approach might be to tabulate a base temperature and height for different climatic zones and seasons and then apply the atmospheric lapse rate (-6.5K per km height) to these values.

On a final note, I don't know what you need this information for. But be aware that for many thermodynamic purposes, the errors that you make are not going to be that large anyway since even an error of 20-30K is only an error of around 10% in absolute temperature. So be sure to evaluate your precision requirements.


Why are "ground level" air quality monitors really on rooftops?

In order to put your question in perspective, you need to consider atmospheric mixing and the size of the system you are curious about.  The troposphere is several kilometers thick and the mixed layer of atmosphere above the ground can occupy a significant fraction of the troposphere.  Granted, during cold events, the temperature of the air can be so cold that the lack of convective motion creates a very shallow (e.g. 10s of meters) mixing layer.  Though, 15 meters above the ground will virtually always be within the mixed layer near the ground, despite cold weather events.

Some people do measure air quality at the ground level, but it is generally done in the context of studying biogenic emissions (e.g. soil emissions or below-canopy tree emissions).  However, those are special field studies, which have different objectives than typical air quality monitor sites.

Typical air quality monitor sites are relatively sparse, and even large cities have just a few monitors.  Generally, monitors are located in such a way that they are observing well mixed air, so that it represents air quality in the region.  It is imperative that you put your air quality monitor high enough above the ground so that it isn't being directly influenced by emission sources (e.g. your neighbors wood stove, or a car's tailpipe).  Otherwise, if you put the monitor literally on the ground, you will often be observing much more variance, with higher peaks that represent that specific location only.  See this post for more info on how monitor locations are selected.  You can also see this EPA handbook on siting monitors which notes: