Will global warming lead to agriculture in the Arctic?

The agricultural regions of Canada:

are located on suitable soil types:

Much of the unfarmed area is the Canadian Shield (shades of red below):

The current surface expression of the Shield is one of very thin soil
lying on top of the bedrock, with many bare outcrops. This arrangement
was caused by severe glaciation during the ice age, which covered the
Shield and scraped the rock clean. The lowlands of the Canadian Shield
have a very dense soil that is not suitable for forestation; it also
contains many marshes and bogs (muskegs). The rest of the region has
coarse soil that does not retain moisture well and is frozen with
permafrost throughout the year.

I think the "coarse soil" is Glacial Till.

This is melting permafrost. I don't think it will be suitable for agriculture.

CLIMATE CHANGE ADAPTATION AND CANADA’S CROPS AND FOOD SUPPLY (2013) (122 pages) discusses warming, precipitation, soils and other factors.

There is definitely potential for a northern expansion of agriculture
as a result of changes in heat and moisture in [the western prairies],
although most of these lands will remain only marginally suitable for
farming because of poor soil conditions.

Edit:
An eroding coastline exposes permafrost along the Mackenzie Delta. Photo: Roger McLeod / Natural Resources Canada
High resolution version from Arctic tundra is 80 per cent permafrost. What happens when it thaws? - The Narwhal:
https://thenarwhal.ca/wp-content/uploads/2019/03/Coastal-Erosion-Permafrost-Roger-McLeod-NRCan--e1551913545654-1920x1080.jpeg
Edit, Oct., 2020:
Canada could be a huge climate change winner when it comes to farmland

In this map from the study, areas that transition from no current suitability for major commodity crops to suitability for one or more crops are depicted in blue, while currently uncultivated areas that transition to suitability for multiple major commodity crops are shown in red. (Hannah et al, 2020)

KC acknowledged the study looked only at climate as a factor when determining where crops could be grown in the future, and didn't take into account things like soil quality.

Soil scientist David Burton of Dalhousie University, who wasn't involved in the study, says that could have a big impact on how much of the potential new farmland is actually arable.
"The soils of Southern Canada have taken 10,000 years to develop," he said. "Rapid change wouldn't necessarily make soils in Northern Canada suitable for production."

Johanna Wandel, an associate professor of geography at the University of Waterloo who edited a book called Farming in a Changing Climate, agrees the soil in much of Canada would likely be unsuitable for growing crops, as it's too rocky or acidic.


Understanding a diagram of Earth's radiation balance

LW = Longwave radiation (i.e. Infrared  radiation)

SW = Shortwave radiation (i.e. Visible + Ultraviolet light)

For the left part of the diagram, it shows that the 30% of the total incoming energy (mostly delivered as shortwave radiation) gets reflected by clouds and the Earth's surface. Therefore, 70% is the total absorbed by the planet (45% of it is absorbed by the Surface and 25% by the atmosphere).

The red line refers to the fact that given that Earth energy budget is assumed to be in balance, meaning that the same amount of energy that goes IN the system (as shortwave radiation) must go OUT the system, in the form of longwave radiation, that is the main kind of radiation the Earth emits given its temperature (see Wien's law). This energy balance assumption is a good one, because even if you think that more energy gets IN than OUT, that would lead Earth's temperature to increase, increasing then the amount of emitted energy (according to Stefan–Boltzmann law), until it reaches a point where the outgoing radiation released equals the amount received energy from the Sun. Therefore, establishing the energy balance.

Due to this balance, the right part of the diagram shows that the same 70% of the energy must be emitted back to space as longwave radiation (the same amount that was absorbed), and for that to be true, it is asking you what would be the required amount of energy that the atmosphere must emit towards Earth as LW radiation to keep the system in balance. Or at least that's my interpretation of the question marks in the absence of further information.


What's the difference between Waste Water Reclamation and Waste Water Reuse?

Water reclamation is the process of capturing, transporting and treating water so it can be be reused, particularly if the water is contaminated. Depending on the type of contamination treatment facilities can be a processing plant or a specially constructed/adapted system of biological wetlands with a slow flow of water through them.

Re-using water is using water more than once regardless of whether it has been treated or not. For waste water to be reused it must first be reclaimed and treated.


Why is smoke considered a factor of global cooling when analyzing nuclear winter scenarios, but arent when analyzing global warming scenarios?

The question contains a false assumption that climate science fails to consider the cooling effects of aerosol particles, including from smoke, that are the principle forcing expected for nuclear winter scenarios. Climate science does attempt to quantify every significant effect, both warming and cooling - not only the warming effect from increased greenhouse gases - $\small\mathsf{CO_2}$.

Whilst not as recent as I would like, the graphic shows the principal climate forcings that climate science has identified and includes the cooling affects of aerosols.

Aerosols from burning forests are short-lived, and the $\small\mathsf{CO_2}$ from burning vegetation should be drawn back down by regrowth, whether forest regrowth or grassland. The most significant, enduring climate consequence of Amazon forest being cleared and burned is not the aerosols or the $\small\mathsf{CO_2}$ from the fires, it is from land clearing turning a large existing Carbon Sink into a carbon source.

Turning the Amazon to savannah would release 200 billion metric tons of $\small\mathsf{CO_2}$. The cooling effect of the aerosols from burning will not persist long, but the raised $\small\mathsf{CO_2}$ levels from the loss of a large Carbon Sink will.


What sort of instruments do planetary geologists use to predict that there are oceans on the moons Titan and Europa?

As casey said, the satellite isn't an instrument in itself, it is a platform hosting many instruments. What you're talking about the discovery of oceans on Titan and Europa, you're referring to the the Cassini-Huygens mission aimed at, among other things :

Determine the composition of the satellites surfaces (such as Titan)
Prove or disprove the existence of oceans or water on those satellites.
Characterize Titan's surface on a regional scale

In order to establish the existence of oceans or water on those satellites, scientists use:

Spectroscopy

More precisely, absorption spectroscopy. AS is an analytical chemistry tool used to determine the presence of a substance in a sample. Thanks to a spectrometer, you'll be able to gather a lot of information about the elements on this Moon, and by further analysis, determine if there's water or no.

N.B : I am not talking about liquid water, but about water. Science now establishes the validity of certain signatures, but liquid water is to be confirmed.

What about Cassini?

Cassini used a lot of spectrometers : the VIMS (Visible and Infrared Mapping Spectrometer) was used on the satellite to gather information on the surface.

  "Our instrument measures the characteristic colors of the many materials that make up planetary surfaces and atmospheres: rocks, ices -- such as water, ammonia, methane -- and organic compounds," says Dr. Bonnie J. Buratti, VIMS Investigation Scientist. "VIMS data makes it possible to know what surfaces and atmospheres are made up of."

Volcanic observation

Indicators such as Geysers can be used to support the theory of water presence in subsoil. If by any radars, such as the ISS from Cassini (Imaging Science Subsystem), you can see geysers, then you can search about the possibility of water. Why? Because as you may know, water is pressurized in subsoil. Then, when water is heated by the heat of rocks, and it will tend to result in a geyser.


The oceans are not saturated in sodium or chlorine. In fact, the chemical makeup of the oceans has remained fairly constant for hundreds of millions of years. Yet rivers and other sources continually bring sodium, chlorine, and other dissolved elements and compounds to the oceans. This means some processes must exist that remove constituent elements at more or less the same rate that the world's rivers and other sources supply these constituents.

It is true that the Earth's rivers bring substantially more calcium and carbonate ions to the oceans than they bring sodium and chlorine ions. Dissolved carbon dioxide from the atmosphere provides another source of carbonate ions. Yet the oceans are indeed substantially depleted of calcium and carbonate compared to the supply from rivers. So why is this?

The oceans are substantially saltier than are the rivers that supply the oceans. This is primarily a result of evaporation. Even with that, sodium and chlorine are but a fraction of their saturation levels in the world's oceans. This is not the case for calcium and carbonate. Carbonates in general have a low saturation level, and amongst common cations, calcium carbonate has a very low saturation level. Calcium carbonate is typically at or near the saturation level in the oceans, and oftentimes above it, particularly near the surface. This makes it very easy for calcium carbonate to come out of solution.

Because calcium and carbonate are at or above the saturation level, just about anything can cause calcium carbonate to form. The key "just about anything" process that causes calcium and carbonate to come out of solution is life.

This means that calcium and carbonate have a rather low residence time in the oceans. The processes that remove sodium and chlorine are much, much slower. The slowness by which sodium and chlorine are removed versus the rapidity by which calcium and carbonate are removed are why the oceans are "upside down" compared to rivers with regard to calcium, carbonate, sodium, and chlorine.


Will we even be burning fossil fuels in approx. 100 years? Worry about climate effects?

Based on the stats for the US only, I would say we have the capacity for using fossil fuels over the next 100 years.  Whether or not we worry about it is another discussion entirely.  The US has coal resources for the next 250 years and I suspect may be producing and using natural gas for the next 100 years.

For the US (from the US Energy Information Administration)

256 years of coal resources available (based on 2014 numbers) link
~12 years of natural gas resources available
Based on resources and current usage rate Link

This is a rough estimate does not take into account importing or exporting natural gas.Also assumes usage based on 2015 numbers.
0 years of oil resources (no surprises here)
US uses 19.4 millions of barrels per day link
US produces <9 millions of barrels per day link

Here is fun stat laden site for global oil and gas resource and consumption. link.

Note: I take these stats at face value but oil resource production and reserve numbers are not always accurate.  A number of oil producers are reluctant to provide accurate stats as these numbers are considered sensitive to their economies.


This can't possibly be right without some type of filtering applied to the total solar irradiance, at the very least. There's a graph here:
https://www.nasa.gov/topics/earth/features/glory_irradiance.html
showing the total solar irradiance measured primarily from satellites (i.e. from space) over the last several decades. There is no such trend in total solar irradiance from 1970 to now as the one shown.

The attribution is to the Berkeley Earth Surface Temperature Project, now Berkeley Earth. You can see a very different-looking temperature graph under their summary of findings:
http://berkeleyearth.org/summary-of-findings/
Reverse image searching the image led me to this thread (https://www.skepticalscience.com/solar-cycles-global-warming.htm) and a couple others, which point to this article (https://www.washingtontimes.com/news/2012/sep/6/global-warming-fanatics-take-note/) as an original source for the graph. That webpage no longer has any graph, from what I can see, but the thread seemed to imply that the graph may have been hand-drawn and with no real sources.


How do I find the horizontal pressure level that divides the atmosphere into 2 layers of equal mass?

I'll answer my own question.
If there is hydrostatic equilibrium, then we have

So, assuming that the surface pressure is p (in this case 1000hPa) and that the atmosphere's top has zero (0) pressure and infinite height, then

In this case:

So, as the atmospheric mass above a surface area is rho times integral of "dz", and assuming that this area is 1 m^2 to simplify (dx.dy=1):

 Pa

If we want half of that mass M, then

 Pa , which is indeed 500 hPa.


Why does Coriolis determine the rotation direction of a cyclone but not my drain?

The answer is the scale.
The fluid movement of a sink has a much smaller curvature radius than the grand-scale movements of a hurricane.
This curvature radius plays a big role on whether your movement due to a pressure gradient will be balanced by coriolis, or centrifugal forces, as thorougly discussed here.

You can read this wikipage, but the essence is the following:

If you transform the equations that govern the movement of a fluid parcel into a coordinate system that moves along with the flow, you find the following force balance that counteracts the pressure gradient:
$$ \frac{v^2}{R} + f v = - \frac{1}{\rho} \nabla P $$

Here we have simply two effects that arise from the translation to a rotating reference frame balancing the pressure gradient. The first one is known as the centrifugal pseudoforce, the second one is the coriolis force.
If you're interested in a derivation, see here, the linearization is then straightforward.

From those terms one can clearly see that fast dynamics (tornado!), or small curvature (toilet) will lead to the l.h.s dominated by the centrifugal term. However for large-scale and slow flows (hurricanes, cyclones etc.) the left side will be dominated by the coriolis term.

As to the question in which direction a certain flow can occur, let's remember that in co-moving coordinates each scalar quantity has however a sign. $v$ changes sign if we turn the wind direction, $R$ the curvature radius sign depends on where the movement is going, $f$ on which hemisphere we are.

If we now take a look at coriolis vs. pressure gradient force (geostrophic balance), we see that this balance has only one direct solution for a sign of $v$ for a given f. Therefore the predetermined direction of geostrophic cyclones for each hemisphere.

However the solution for $v$ in the cyclostrophic case is obviously
$$v = \sqrt{-\frac{R}{\rho} \nabla P} $$

Which can never be satisfied for high-pressure systems as then curvature sign and gradient sign cancel out and leave the term imaginary.

However one can easily visualize where the forces are pointing:

($F_p$ is the pressure gradient force, $F_c$ Coriolis and $F_{ce}$ the centrifugal force. + and - denote the root's solution. Property of K. Roth, Heidelberg University.)

Here in the case of cyclostrophic balance just look where the red arrow can cancel the green one and you see how Tornadoes do not care for the hemisphere, but just for low-pressure systems and can go in any direction around this.


How common is it for warm fronts to be responsible for record high temperatures?

I'm not sure that record temperatures occur with enough frequency that we can attribute them to this specific phenomena, though I am no authority on records.

I think that attaining a record high with warm frontal passage is unlikely.  As you mention, the warm front is often accompanied by cloudy weather, and these clouds first manifest as high cirrus clouds well poleward of the surface front, lowering closer to the front.  This combined with possible precipitation will moderate the temperatures.  If rain is falling on the cold side of the front prior to frontal passage (FROPA) it will likely stabilize at the wet-bulb and stay far from the record temperatures.  Places on the warm side, if experiencing precipitation will have a similar experience and probably not near high temperatures.  You may have a case here for potentially warmer night temperatures, but I'm not sure you'll be in record territory.

A better case for potential high temperatures can be just prior to cold frontal passage.  Especially for a strong cold front, the pressure gradient can drive strong southerly flow (in the NH) bringing lots of warm air advection ahead of the front.  If you watch a temperature trace before such FROPA you'll see a sharp rise in temperature before it plummets.

Typical FROPA probably won't set records.  You need some exceptional aspect of it or at least some unseasonable occurance to really threaten the records.

Though I was unable to justify it with data, I do not suspect warm frontals are a common source of high temperature records (high daily max or high daily low).


how much entropy is there in the shape of a rock?

This would actually be an interesting research project.  The ability to do a 3-D scan and model of a rock's shape in a manner that is fast enough to get a statistically meaningful number of rocks measured is relatively new.  Certainly you can easily name things that probably control the shape of rocks (lithology, mode of erosion, mode of transport, history of these things) but saying something about this in a quantitative way has not been done AFAIK.

I think this is the sort of question that might result in surprising insights.  Or it might lead nowhere.  Certainly the question 'Does arid region erosion result in different shaped rocks than glacial erosion?'  answered in a quantitative way sounds worthwhile to me.


Does the Columbia Basin, North Cascades and Idaho Panhandle/Clearwater get a lot of violent, rotating supercellular thunderstorms?

Note: These inputs are of a meteorologist who has quite actively tracked severe
weather around the US for a couple
decades... but also hadn't spent as much time in or attention on the west and northwest of the country.  So there could could theoretically be some degree of unintended bias.  I've tried to use objective data where possible, but it's something to keep in mind, regardless.

In terms of "a lot", at least for tornadoes/hail, I'd suggest that nowhere west of the Rockies sees a lot truly:

Tornadoes [left], hail [right]... source: Storm Prediction Center SVRGIS page
I'd offer the idea that there of perhaps two notable reasons for that:

The lack of consistent and widespread surface wind fields favorable to tornadoes... due to a combination of the lack of the big lee cyclogenesis seen east of the Rockies, along with the complex topography.
The rarity of regular low-level moisture significant enough to allow for low LCLs.  The higher the storm base is above the ground, the less likely a tornado
[in figure 6 of Edwards and Thompson 2000, all the red boxes showing tornadoes are on the left side, which are lower LCLs]

The lack of rich low-level moisture is due in large part to the lack of accessibility from warmer moisture sources, particularly the Gulf of Mexico; the Rockies provide a barrier to much of the moisture reaching further west.
As you note, parts of Wyoming and Montana do see supercells and tornadoes a bit more often... but on a good topographic map, fair parts of those states are east of the Continental Divide, and so still on an "upsloping" area and thereby not blocked by sinking regions which prevent full moisture progress.  They're still less-tornado prone due to elevation and increased distance from moisture, but it does happen.
The desert southwest also does manage to get monsoon moisture sneaking around the terrain further south... but further north that monsoon moisture sees additional blocking by the more elevated terrain across Nevada and Utah.  (And in the southwest, a different key ingredient in tornadic supercell development is typically missing in the summer monsoon: upper-air winds sufficient for supercell development)
The Pacific Coast does see a few occasional tornadoes.  But from what I've seen, they typically form from smaller storms with much less classical and intense mesocyclones.  As you mention, they're a bit more in line with cold-core setups, which usually produce weaker short-lived tornadoes than classic supercells of the Plains and on east.  If you plug in the events you speak of into SPCs Severe Weather Events archive, [pick the date, then click Obs and Mesoanalysis on the left, then use the dropdowns to find various parameters]
you can see that CAPE was typically very meager (well short of 1000 J/kg) and the storm structure quite weak in reflectivity in comparison to a classic supercell, more indicative of such cold-core setups.
Capping inversions may be helpful to "keep the lid on the pot" if you have strong CAPE (and therefore quality moisture) and intense updrafts to erode the cap during the day.  But as it is, there isn't enough moisture typically for the cap to be a positive factor.
Quite a bit of the area you mentioned, such as Spokane, makes up one of the more arid regions of the country.  Further removed from the rain shadow of the Cascades, the Idaho Panhandle does get more precipitation, but it's mostly in cooler months that lack the deep convective development needed for supercells, probably because of the proximity of the Pacific high in summer months.  Further east in E ID/W MT, particularly in the Flathead Valley, there are occasional summer thunderstorms... but the storms may well be higher based due to the limited low-level moisture, and so have low potential of tornadic development.  For more on the climate complexities at play in the Idaho vicinity, check out this article from the Western Regional Climate Center.
In the end, the area you ask about is one of the weakest regions for supercell development in the entire United States.  The entire quarter of the country west of the Rockies isn't much better.  This page from Iowa State University will create an image showing the number of days since each pocket of the country (sorted by National Weather Service forecast office areas) last saw a tornado warning.  It shows that the Idaho Panhandle hasn't seen a tornado warning in over 10 years:

More than anywhere but northern California.
And another ISU page allows it to be shown that:

... Most spots in northern Idaho only see about one severe thunderstorm warning per year on average (versus 10-20 per year over Oklahoma).
So the danger of severe convective weather is remarkably low in that region of the country overall.


Is fracking likely to produce earthquakes?

It's not too too likely, but it can happen. A few earthquakes have either been attributed to fracking, or the wastewater produced from fracking.

According to the USGS (for more information, visit that page):

  Many questions have been raised about whether hydraulic fracturing —
  commonly known as “fracking”— is responsible for the recent increase
  of earthquakes. USGS’s studies suggest that the actual hydraulic
  fracturing process is only very rarely the direct cause of felt
  earthquakes. While hydraulic fracturing works by making thousands of
  extremely small “microearthquakes,” they are, with just a few
  exceptions, too small to be felt; none have been large enough to cause
  structural damage. As noted previously, underground disposal of
  wastewater co-produced with oil and gas, enabled by hydraulic
  fracturing operations, has been linked to induced earthquakes.

So, indeed, the disposal of wastewater has been linked to earthquakes.

This study in Geology also came to the same conclusion that the wastewater of fracking caused earthquakes.

An additional study produced by Ohio geologists concluded that a few small earthquakes were due to fracking. I do wonder exactly what they concluded, but the news story doesn't say. It does say this, but it doesn't say exact what this study was, either:

  A U.S. government-funded report released in 2012 found that two worldwide instances of shaking can be attributed to actual extraction of oil and gas, as opposed to wastewater disposal in the ground — a magnitude-2.8 quake in Oklahoma and a magnitude-2.3 quake in England. Both were in 2011.


Vortices in the Atmosphere Sustained only by PGF

Tornadoes, land/waterspouts and supercell thunderstorm mesocyclones are examples of vortices where Coriolis is unimportant.  Tornadoes are in cyclostrophic balance.  Land and waterspouts (as well as non-supercell tornadoes) arise from horizontal convergence of vertical vorticity.  The supercell mesocyclone spin originates as horizontal vorticity that is tilted and stretched into vertical vorticity.  These vorticies can spin in either direction in either hemisphere.


Is there a better system than latitudes and longitudes?

To answer the question in the title (with another question): better for what?

To answer the questions in the body: there is another system called rectilinear or x-y-z Cartesian coordinates. This system is the same as the 3-d Cartesian coordinates you used in calculus class. The center of the Earth is (0,0,0), and generally the z-direction goes to the poles, while x and y go to the equators at right angles, and towards points specified by whatever datum you are using. For example, the North Pole could be coordinates (0,0,6353000) in meters.

As for tools to do the conversion, you should probably send those kinds of questions to the GIS Stack Exchange. However, since they tend to be snippy when I've asked questions in the past I will tell you how I do it. I use the pyproj package for python, which is a python wrapper around the C Proj4 package.

Pyproj is pretty straight forward to use, but the transformations you want might not be so easy to figure out. The information you want is the EPSG numbers of the transforms. If you are using the common WGS-84 datum, for example, the rectilinear is 'epsg:4978' while the lat-lon is 'epsg:4326'.

A code example follows from a python (3.5) interactive console. To transform the estimated coordinates of the north pole above, we want to go from rectilinear to lat-lon.

>>> import pyproj
>>> rectProj = pyproj.Proj(init='epsg:4978')
>>> latlonProj = pyproj.Proj(init='epsg:4326')
>>> pyproj.transform(rectProj, latlonProj, 0, 0, 6353000)
(0.0, 90.0, -3752.3142451792955)

What we see here is that when we transform from rectilinear to lat-lon, we get a longitude of 0, latitude of 90, and azimuth (or altitude) of -3752.3 meters. So at the North Pole, 4 km below the surface. This altitude difference is due to both the roughness of my estimate and the specifics of the WGS-84 datum. To go the other way from the lat-lon Greenwich Observatory (at 51.478 degrees north, 0 degrees west; azimuth or elevation of 0 meters) we can do:

>>> pyproj.transform(latlonProj, rectProj, 0, 51.478, 0)
(3980563.802812242, 0.0, 4966838.380583356)

We see that 90 degrees north at altitude of zero in WGS-84 corresponds to that (x, y, z) triplet, in meters. Since Greenwich lies on the 'y' coordinate axis in the WGS-84 system, the y coord is zero.

Hope this helps.


Is there a concise industry standard way of displaying wind speed, gust and direction?

The colon could be problematic as its function is not directly clear in this context. Maybe you could try:

N @ 8 m/s | G: 12 m/s

or a little shorter:

N 8 (12) m/s

The way you created it should not be problematic. However, especially if you make it possible for users to chose the unit, the unit should always be displayed. Here, the standard way in meteorology is to use meters per second [m/s], which is also complying to the SI units (more info here).

The wind direction is often divided into two directional vectors, $u$ and $v$. However this is more science standard than industry standard as the ouput in degrees is more intuitive to interprete. In meteorology and climatology, the direction is only expressed in direction charcters (N, NE, E, ...) if the measurement or model output is not accurate enough to express it in directional degrees or if very exact values are not required, as for example in popular weather forecasts. (Notice that you can make it more exact by dividing the scale into smaller steps: Between N and NE, there is NNE, NEE, and so on).


To form a cloud, generally speaking, pressure does not play a significant factor (with the exception of the effect on temperature).

If you want to prove that to yourself, go outside when it is foggy and measure the air pressure. Fog is a cloud, and pressure likely hasn't changed as much as it does in the vertical direction.

As far a forming a cloud, it is actually relatively easy. You only need to decrease the temperature to it's dewpoint. You may want relatively dirty air, so that the solute effect makes it easy to form drops. Silver iodide would be good for this.

For reference, the Kohler curve describes the saturation needed for cloud droplets to form.


Where did all the CO₂ come from that was deposited as coal during the Carboniferous?

Prior to the carboniferous period, and until the human industrial era, the majority of carbon dioxide in the atmosphere was due to volcanoes.

  For billions of years, geological processes like volcanic eruptions controlled the carbon concentration in the atmosphere, as volcanism is the major way that carbon rises from the mantle into the atmosphere. Most of the carbon stored in the mantle is in the form of carbonate (a salt of carbonic acid), but there are also huge stores of actual carbon dioxide sequestered deep within the mantle as a dissolved gas within the liquid rock.

  We can measure the degassing of Mt. Etna extremely well, and find that it adds about 16,000 tons of CO2 to the atmosphere each day, or 5.8 million tons per year. This might not sound impressive, but it's only one volcano. Interestingly, it's not just smoking, actively erupting volcanoes that emit CO2, but seemingly inactive volcanoes around arcs and rift zones. Persistent degassing still occurs around a great number of volcanoes worldwide, on a continuous basis.


I just came upon this paper by Tomascik et al. (1996). It does not answer directly your question, but I found it sufficiently relevant to post it here anyway. It's a study of coral colonization after emplacement of a lava flow. They found that only five years after emplacement of an andesitic lava flow from Gunung Api in 1988, large parts of this new coastline was already covered by coral. They mention a previous study (Grigg and Maragos 1974) on Hawaii where it took 20-50 years for coral to colonize a submerged lava flow.

Of course, in those cases the coral could come from nearby reefs, while in the case of a new island it has to come from further away. So those fast colonization timescales are not directly applicable to your question.


Is it true that earthquakes are not felt in a cave?

Ground motion results due to passage of elastic waves. Now there are different kinds of waves, e.g., P waves, S waves, surface waves, etc. Most of the shaking (and therefore damage) is caused by surface waves. So if you are in a deep cave or mine then the amount of shaking you might experience can be much lower than on the surface. This of course assumes that the mine or cave is not right on the fault, but some distance (10s of km) away.

Having said that, there are some situations (based on the type of earthquake and local geology) in which waves can interfere constructively to cause significant shaking even at large depths, but this only happens in certain areas and not everywhere.

In many ways elastic wave propagation is similar to acoustic or water waves. Imagine a small explosive source in a lake/pond, let's say 50 m underwater. After the explosion there will be more 'shaking' on the surface, but not much underwater unless you're extremely close to the explosive source.


Since the sound waves propagate not from a single point source but along the length of the lightning's path, the sound origin's varying distances from the observer can generate a rolling or rumbling effect. Perception of the sonic characteristics is further complicated by factors such as the irregular and possibly branching geometry of the lightning channel, by acoustic echoing from terrain, and by the typically multiple-stroke characteristic of the lightning strike.

In addition, thunder will sound louder when the air near the ground is colder and the air higher in the atmosphere is warmer. This is because the acoustic shock waves get "trapped" in the cold air. Such a temperature difference, called an inversion, tends to happen at night, which is why thunder often sounds louder at night.

See Wikipedia and wisegeek.org


How do I convert Relative Humidity into specific humidity

The Engineering Toolbox gives the following equation for converting relative humidity to specific humidity.

  If we know the relative humidity of the moist air and the water vapor density and density of the air, the specific humidity can be expressed as:

x = 0.622 φ ρws / (ρ - ρws) 100%         (1)

where

x = specific humidity of air vapor mixture (kg/kg)

φ = relative humidity (%)

ρws = density of water vapor (kg/m3)

ρ = density of the moist or humid air (kg/m3)


Is it possible to convert carbon dioxide into a solid at low cost?

This article in the press cites CO2 has been converted  into Carbon at relative low temperature.

The research team led by RMIT University in Melbourne, Australia, have developed a new technique that can efficiently convert CO2 from a gas into solid particles of carbon.

To date, CO2 has only been converted into a solid at extremely high temperatures, making it industrially unviable.
By using liquid metals as a catalyst, we’ve shown it’s possible to turn the gas back into carbon at room temperature, in a process that’s efficient and scalable.
While more research needs to be done, it’s a crucial first step to delivering solid storage of carbon.

The study is published in Nature Communications. They used cerium oxyde as catalyst to reduce the temperature of the reaction.

... which facilitates the electrochemical reduction of CO2 to layered
solid carbonaceous species, at a low onset potential of −310 mV vs
CO2/C

All these CO2 sequestration issues are under investigation and will be implemented in the coming years by energy companies.

Esrafilzadeh, D., Zavabeti, A., Jalili, R. et al. (2019): "Room temperature CO2 reduction to solid carbon species on liquid metals featuring atomically thin ceria interfaces." Nat Commun 10, 865. https://doi.org/10.1038/s41467-019-08824-8


Is there any natural phenomenon that can account for the 100 fold faster CO2 rate increase?

There are things that could do it, but they would be very noticeable.

A whole string of thousands of the largest volcanic eruptions to ever occur on earth. Volcanoes can briefly match human output, but volcanoes only erupt for a few minutes or hours. you would need the equivalent of mount St Helens erupting continuously for 70 years.
The burning if every forest on the planet. I'm sure humans would notice such a firestorm, and I;m not even sure if that would be enough.
The slow extinction of photosynthetic algae. without the bulk of photosynthesis to convert co2 into oxygen it could build up. Of course we monitor this so it would not go unnoticed. More importantly oxygen levels would also drop dramatically which it hasn't. https://scied.ucar.edu/imagecontent/carbon-cycle-diagram-ipcc
The ignition and continuous burning of millions of miles of exposed coal beds. You would need to burn approximately ~3 teratons of coal. coal beds can be ignited by natural causes but we would notice a coal fire larger than a continent.
A drop in sea level by hundreds of feet would do it. large sea level drop cause outgassing of methane and co2, from wetlands and sea muds. but again New York would notice it if long island stopped being an island.

Some of these have actually happen in earth's geologic history just none recently, and not as consistently as we have managed (at least not since life evolved). Really scientists have spent a lot of time and money looking for other causes, and no one has found any. Scientists concluded it was human activity by eliminating every other conceivable cause.


What is the relationship between Ekman transport and Ekman pumping?

The link is the conservation of mass equation: $$ \frac{\partial u}{\partial x} + \frac{\partial v}{\partial y} + \frac{\partial w}{\partial z} = 0. $$
Briefly, Ekman pumping is the horizontal divergence of the Ekman transport.

If we integrate this expression over the Ekman layer,
$$ \int_{-D}^0 \frac{\partial u}{\partial x} dz + \int_{-D}^0 \frac{\partial v}{\partial y} dz + \int_{-D}^0 \frac{\partial w}{\partial z} dz = 0$$
where $D$ is the height of the Ekman layer we take $z=0$ to be the surface.  This gives
$$ \frac{\partial}{\partial x} \int_{-D}^0  u dz +  \frac{\partial }{\partial y} \int_{-D}^0 v dz + w_{z=0} - w_{z=-D} = 0$$ or,
$$ \frac{\partial}{\partial x} U +  \frac{\partial }{\partial y} V + w_{z=0} - w_{z=-D} = 0$$
where $U$ and $V$ are the horizontal volume (Ekman) transports.  As a boundary condition we take $w_{z=0} = 0$ leaving
$$ w_{z=-D} = \frac{\partial}{\partial x} U +  \frac{\partial }{\partial y} V. $$
So, the Ekman pumping velocity is the horizontal divergence of the Ekman transport (and thus also proportional to the curl of the wind stress).


MODIS Surface Reflectance Data State QA Aerosol Quantity field meaning is unclear

Most likely, climatology means there was no retrieval at all.

Bayesian retrievals combine information from an a priori with information from measurements.  When the retrieval fails for whatever reason, or the measurement contains insufficient information, instead of reporting no measurement at all, they copy over information from the a priori and use the flag to indicate that the reported "measurement" is in fact simply the climatology.

For details, see optimal estimation, in particular the book by Rodgers:

Clive D. Rodgers (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific.


I was about to ask a question whether this should be classified as a sinkhole or not. This is because this so-called 'sinkhole' is not entirely because of a natural process. It seems that it was the underground GPO Station (or the tunnel) of Orange Line Metro Train Station that collapsed.

I can't seem to find an article that specifically mentions this. One news article I found isn't exactly very clear about it, but suggests this to be the case.

I think you can blame the 'sinkhole' (or collapse) or the failure of the underground structure. One of your source's title "Those responsible for Lahore sinkhole will be punished" also seems to suggest that it has only man-made causes, and not natural.

Further sources:
Punjab Mass Transit Authority - Orange Line Metro

P.S. I live here. :)


Does the length of a river depend on the scale of measurements?

The same problem applies to all shapes that are complex and this includes coastlines, rivers and the perimeters of leaves. Most shapes derived by nature will have this issue. The unit of scale matters.

Only shapes that are composed of straight line segments and can be easily measured as such do not have this problem: triangles, rectangles, hexagons, etc. Such shapes are mathematical or created by humans.


What is the relationship between raw seismic data, CMP Stacking, NMO & Machine Learning

SEG-Y files are a general purpose archival format. So they can be "anything". 2D seismic, 3D seismic, raw, processed, unstacked or even derivatives (seismic attributes).

How to get information about your data in the SEG-Y file:
SEG-Y contain 3 types of headers that describe the data.

The EBCDIC header is a 40 by 80 character sheet that is a remnant from the times of punchhole cards (I'm not joking). Usually it contains info about the state of the seismic. Processing workflow, acquisition geometries etc. The binary header has some more fixed data about the data set. The trace headers contrain every separate data set from the acquisition. Nice blog by Agile Scientific here.

You can read about the details of seismic processing and conventions in the open access book "Seismic Data Analysis". It used to be several hundred dollars and has been cited in every seismic lecture I have ever attended. However, I'll give you some answers now.

Common Midpoints

CMP stacking, weighted CMP and NMO are highly entangled.

A seismic measurement, listens really close to the reflection from the subsurface. However, it's a little noisy. "Stacking" is recording the same place over and over, then adding it up to cancel out random noise and enhance coherent signal. However, just doing it in the same area would be very inefficient, therefore we change the acquisition over the point we illuminate, in the picture you see that we have a nice fan of ray geometries going to the same point in the middle, the Common MidPoint CMP. This leads some interesting behaviour.

Normal Moveout

this is the formulation to calculate how a record from a different angle can be shifted to be in line with another record for the aforementioned stacking. As you can imagine the records that have longer ray paths take longer, depending on the velocity of the rock they travel through. This shift down-ward is namen Normal MoveOut (NMO). The equation above is an approximation, but it's good enough for this explanation, check out the book for more info on this.

Post-Stack vs. Pre-Stack

A seismic acquisition over a 2D area is named 3D seismic, because X-Y-T (recording time). The data, repeated over the same point in X-Y as mentioned above will have several record with the same T and a different "offset" between the source and the receiver. That means, 3D seismic data coming fresh out of the field will be four-dimensional. This is called pre-stack. Once we do a lot of data preconditioning and filtering and a smarter version of the CMP stacking above, we get 3D seismic that is actually three-dimensional.

Stacking versus Migration

Stacked data will have all kinds of weird wave effects in it. Usually geologists will work on "migrated" data. A migration aims to find the "true" location of a reflector, reversing wave effects. I suspect you might get this one.

Time versus Dpeth

You may have wondered from before about X-Y-T. T stands for time not depth, that is because our data is measured in recording time and most geophysicists will work in time. Most likely the migration even is done in time and you get a X-Y-T cube. However, depth-conversion does happen, but it depends on a accurate time-depth relationship. People tend not to trust these too much. Caveat: A depth migration does not have to result in a X-Y-Z cube that you get.

Practical Advice

As a practical guide, you may want to start at the EBCDIC header. Was your data processed? Stacked? Migrated? Do you have a T-cube or Z-cube?
Familiarize yourself with the binary header and a couple trace headers. What are the extents of your data? Etc.

Machine Learning

Most machine learning is done in proprietary code. The only open source code I know of in seismic deep learning is MalenoV. However, loading a full 3D seismic into RAM will not always be possible. Loading it into the GPU RAM will seldomly be possible. This is the first place, where you are free to come up with a smart solution.

Bonus

Here is a list of Free Geophysics Software and an awesome list of open geoscience. These aren't exhaustive, but a good start.


Why is the environmental quality index high in USA, the second most polluting country?

The website you cited has this explanation:

  Most of our data are based on perceptions (opinions) from visitors of this website. For pollution section, we include some relevant data from World Health Organization and other institutions if we find it helpful.

So basically that index is based on people's perception, and CO2 pollution (which your "1/3 of the world total pollution" is probably based on) is not something that people can see or smell, it's problematic because of indirect effects.

The USA and Europe have pretty strict regulation to reduce the more perceptible components of vehicle and industry exhausts, like soot, sulfur oxides and nitrogen oxides. Their cities also often have good public transportation and zoning laws that separate industry and residential areas.


Why is there no middle ground between tornadoes and hurricanes?

Tornadoes are the result of small-scale effects such as the convergence of updraft/downdraft regions in a single thunderstorm, the stretching or entrainment of vertical vorticity, wind shear profiles, and even friction with the ground.

Hurricanes rely on massive amounts of latent heat release from an atmosphere moistened by warm ocean waters causing rising air and lower pressure over a wide area.  This allows the Coriolis effect to act, sculpting airflow into a spiral pattern.  Under normal conditions, the Coriolis effect is only applicable over a large area (don't get me started on the toilet myth).

There are some intermediate types of circulations.  A mesocyclone is roughly city-sized (and these are often associated with tornadoes).  A mesoscale convective vortex can form out of a thunderstorm complex and are roughly state-sized (usually more of an Indiana than a Texas).  There have been very small hurricanes and typhoons (look up Tropical Storm Marco or Typhoon Tracy).

Extending the scale upward, the normal "synoptic" low pressure systems associated with cold and warm fronts and usually bigger than hurricanes, and upper-level circulations can be continent-sized.


Model types: Robust diagnostic versus prognostic

As far as I know in the ocean modeling community the terms diagnostic and prognostic have quite different meanings from what you are stating.

A prognostic simulation is used to predict future state of the system (forecast) using the model equations. The model is initialized with the initial conditions and it is used to predict using the Navier-Stokes equations the future ocean state under specified boundary conditions. There is no constraint by observations because the model numerically follows the specified equations. In the case of models with data assimilation, the prognostic solution is statistically modified (e.g., Kalman filter, adjoint method) to optimally adjust to the observations, but the new solution can't be considered prognostic as there has been a departure from the primitive forward (Navier-Stokes) equations.

On the other hand, a diagnostic simulation does not provide a prediction of the fluid state. There is no time-evolution based on the model equations. An example of such simulation is the case in which the velocity field is calculated based on a fixed density (temperature and salinity) field. While these simulations can be useful for short-term ocean state estimation, they can't be used for ocean state evolution as the dynamics are restricted.

As far as I understand it, this is the standard in the ocean modeling community, but I don't know if it can be expanded to other fields.


"Impression and pseudomorphism" in fossils

Impression are just that, an impression, an imprint of the fossil but the fossil that created it is gone (or just separate) such as this impression of a ammonite.
Pseudomorphism is just a new term for a natural casting it can be a cast of the fossil or a cast of the fossils impression, again the shape is preserved the the original fossil is seperate or has dissolved and been replaced, for instance the natural casting of the braincase of hominids can be used to reconstruct missing parts of the skull. This snail shell is also a pseudomorph, the entire thing has been replaced with agate.
In true fossilization some component of the original material is still present this can preserve internal structure. With bone for instance a ture fossil of a bone often has some of the original calcium/phosphate with pore spaces around it filled in with other minerals.  Of course like anything in nature pseudomorph and true fossil are not completely discrete but ends on a spectrum, partial replacement is the norm.


Calculating 30% less solar energy. Provided the math is right, would there have been liquid water on Earth?

Quick and simple answer: A greater greenhouse effect.

There is an equilibrium in place. CO2 weathering rates depend on temperature, so at lower temperatures, we see CO2 levels rise, warming the planet. At higher temperatures, higher weathering rates mean more CO 2 drawdown, so the planet cools.

This is a very long term process. But this CO2 thermometer effect is believed to be the reason why we have had liquid oceans for all of known earth history.

As far as the black body effect goes; this is correct - from the viewpoint of an observer at distance, Earth would 'appear' colder. However, this is because the emission height changes (see https://andthentheresphysics.wordpress.com/2014/03/05/effective-emission-height/ ) for more of an explanation. Think of it as 'the height at which the atmosphere becomes IR-opaque' (although this is a simplification); the temperature then follows the lapse rate down to the ground. So raising the emission height raises the surface temperature.

(NB late at night, add refs later..)


Understood cause of sea level rise between 1850 and 1950

SLR is thought to be caused by temperature rise, which is caused by CO2 rise and other factors. Temperature has been rising since before 1850. It could be another Question: "Why temperature does not correspond with CO2 levels since 1850".

  The reconstructions used, in order from oldest to most recent publication are:
  (dark blue 1000-1991): The Holocene.
  (blue 1000-1980): Geophysical Research Letters.
  (light blue 1000-1965): Ambio. Modified as published in Science.
  (lightest blue 1402-1960): J. Geophys. Res..
  (light green 831-1992): Science.
  (yellow 200-1980): Geophysical Research Letters. doi:10.1029/2003GL017814.
  (orange 200-1995): Reviews of Geophysics. doi:10.1029/2003RG000143
  (red-orange 1500-1980): Geophys. Res Lett.. doi:10.1029/2004GL019781
  (red 1-1979): Nature. doi:10.1038/nature03265
  (dark red 1600-1990): Science. doi:10.1126/science.1107046
  (black 1856-2004): Instrumental data was jointly compiled by the Climatic Research Unit and the UK Meteorological Office Hadley Centre.
  Robert A. Rohde,  Creative Commons Attribution-Share Alike 3.0 Unported


It is called an aquarium, put what ever you want in it. With natural waters you have a good chance of collecting dragonfly larva , which will eat about anything else you catch including small fish. Chances of mosquito larva are low in my experience. I don't know why you would not want frog or toad tadpoles ; I have a couple very small feral ponds in my yard and various frogs and toads  deposit uncountable numbers of eggs > tadpoles. Some years I have taken thousands of tadpoles to nearby lakes and dumped them . When I don't do that most get eaten by siblings. You are unlikely to find suitable native plants other than duckweed, so I suggest buying Hornwort at a pet shop -pretty bullet proof.


To actively take measurements from the mount of an erupting volcano is not practical, so the mechanisms are often modeled in a laboratory, such as what was performed in the article Experimental generation of volcanic lightning (Cimarelli et al. 2014), who determined that volcanic lightning is controlled by the dynamics of the volcanic jet and how much fine particulate matter is present.

A key observation that the researchers found was that the movement of clusters of charged volcanic ash and other particles form to generate an electric potential, critical for the generation of lightning. An idealised diagram of this process is shown below:

Image source

As to why the particles themselves are charged was researched in the article Electrical charging of volcanic ash (Aplin et al. 2014), which suggests the ash becomes charged through fractoemission, triboelectrification, and the “dirty thunderstorm” mechanism, and the authors also suggest that natural radioactivity in the volcanic emissions may play a role.


Is it ever possible for an continental plate to subduct under an oceanic plate?

No it is not possible because the continental crust's density is lower.
Conversely, oceanic plates can subduce under continental ones because they are heavier. This is because continental crusts are formed by granites and sedimentary materials. Their density rounds $2,7g/cm^3$, granites ones, while oceanic crust rounds $3g/cm^3$, basalts and gabbros ones.
Oceanic plates start to subduce at a certain age. Presently there are not pre-Jurassic oceanic plates that do it. This is related with the stack of hidrated sediments over the igneous materials.

An exception to this are ophiolites, where oceanic plate obduce continental one. This is not happening presently at any place on Earth, but there are fossilized examples as Semail Ophiolite at Oman.


Spectral Whitening, Seismic Interferometry

I'm not an expert on this topic. But my understanding of seismic interferometry is that we try to obtain the 'Green's function' between various receivers. This Green's function is the equivalent of sending a spike pulse (i.e., delta function $\delta$) at one receiver, and recording the result at the other receiver. Once we have plenty of these Green's functions, ideally surrounding an area of interest, we can compute arbitrary Green's function recordings by evaluating a set of convolutions (rather than carrying out computations). It will be as if there is a recorder anywhere we want to record the signal.

In the time-domain, this $\delta$-spiked source is a source that's on for an infinitesimally small amount of time before switching off again. As you can maybe imagine, this source will have an infinitesimally small wavelength. We will thus be able to see every modulation of the signal as a single identifiable event in time: a scatter of amplitude $A_1$ happens after $t_1$ seconds, a reflection with amplitude $A_2$ happens after $t_2$ seconds, etc. Thus, we gain an incredibly high-resolution understanding of the medium in-between!

The frequency spectrum of such a $\delta$-spike has uniform amplitude. All frequencies are thus represented. It is called a 'broadband signal'.

In reality, we do not obtain $\delta$-spikes very often because the predominant frequencies in the data are provided by noise with a specific frequency, usually a very low one. This low-frequency, or long-wavelength, property makes the obtained Green's functions of very low frequency. Different events will smear and overlap in time. This makes it more difficult to interpret the medium from the obtained Green's functions.

Spectral whitening is a process that boosts frequencies with a low amplitude, to try and make any signal more $\delta$-pulse-like. See for example here https://twitter.com/DownUnderGeo/status/984324387720527872 for an example from active seismic exploration, how boosting the amplitude of higher frequencies (while retaining the low ones), doesn't just look nicer, but provides a considerable increase in identifiable events! The same principle applies to seismic interferometry. By filtering the data, we obtain a more $\delta$-like behavior, thus obtain a more broadband signal from a less-than-ideal signal.


Is there an atmospheric data database somewhere on the web?

By your edit, it seems you are more interested in the "big data" side of the science. Model reanalyses contain large amounts of historical weather data, so that is a safe bet for ample amounts of data. NOMADS, which is a service provided by the National Weather Service is a good resource for model data. There is another version of NOMADS for operational data; I'd recommend that. For generic data, RDA has data. NASA has a server that also has a lot of data.

You can also generate your own weather data for prediction and the like, by using models like WRF or SPEEDY.

Departing from model data, you have observations, you can access ground-based observational datasets via NCDC. NCDC also has satellite data, radar data, and so on down the line (you don't need me to explore the site for you).

Other countries have other datasets, though many come with a price. ECMWF, for example, has reanalysis products, operational datasets, etc. The same is true for JMA, and other countries with their own weather service.


The precise reasons for leaf shedding after monsoon aren't well known and studied, but there are vague reasons like optimising use of light and warmth for max photosynthetic gain. So the tree has a heavy load of leaves to grow fast in monsoon, and it fast depletes the groundwater if it keeps the leaves in the dry season. read the conclusion here: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2006.00213.x
Trees need to store reserves to allow their survival during winter and for bud flush and leaf growth in the following spring.
Generally it takes 2 weeks or a month for a deciduous tree to prepare dormancy/sleep time, to gather all the nitrogen, glucose and nutritive elements from their leaves.
If the plant keeps on growing when it's leaves are adapted to massive rainfall, it may lose the green leaves rather than gather useful elements and prepare for dormancy.
i.e. from this image you can see the winter stored sugar being used to grow new leaves, for this tree the spring is in april:
There's some interesting facts about leaves here: https://en.wikipedia.org/wiki/Autumn_leaf_color#Function_of_autumn_colors


If there's more water in the atmosphere due to global warming, how are there fewer mountain glaciers?

Indeed some glaciers are growing and gaining mass due to increased precipitation (in part due to climate change and enhanced atmospheric water content). But that's the case only for glaciers in very cold places, like East Antarctica, where most glaciers appear to be growing at increasing rates (1,2). But unfortunately West Antarctica is pretty much a desert; the total precipitation on average is less than in the Sahara Desert. That is usually the case in very cold places. The capacity of air to hold dissolved water decreases exponentially at lower temperatures. Therefore cold glaciers (the ones that are most often growing) account for a small amount of the total global ice mass change.

In contrast, temperate glaciers (i.e. not so cold) constitute the majority of the most well known mountain glaciers (outside Antarctica and the High Arctic). These in many cases are receiving more precipitation, but the increases in accumulation are in most cases offset by the increase in temperature, which have a double impact:

More melting due to higher temperatures
A larger fraction of the precipitation falls as rain instead of snow, therefore promoting melting instead of accumulation.

That last point is not that intuitive, but as explained in this question and answer, warmer air carrying more moisture will develop water droplets quicker, therefore dropping most of the extra moisture at lower elevations. As a consequence, the rain-driven melt in the lower areas of the glacier will overcome the a moderate increase in the accumulation at higher elevations.

Also, there is a positive feedback kicking in against the glaciers: The more the glacier melts, the lower its surface will be, therefore it will be exposed to warmer temperatures leading to further melting, leading to more surface lowering, exposing it to even higher temperatures, producing even more melting and so on.

It is also important to note that glaciers grow due to snow accumulation. It is unclear to me if that's what you meant by "moisture condenses at mountain tops".

Finally, it worth noting that glaciers have been receding since the last ice age about 20,000 years ago (as we have learned from Oxygen isotopes on foraminifera deposits and glacier deposits), the recession slowed down the last ~6,000 years slowly approaching to a steady state. However, recent warming has just accelerated the process a bit, enhancing the extent and speed of the natural deglaciation, but we have to acknowledge that the glaciers were already out of balance and shrinking way before the industrial revolution.


How can I calculate the sensitivity of a seismometer?

create a bench test using an acceleramentor and a transducer since their values are known then equate the results to your sesimo


I found this strange spherical stone in Gillette Wyoming. I have no idea what it is. Please help.

This is a so called Moqui Marble. A spherical concretion formed by sand and iron based minerals like limonite.

Further information can be found here


It looks very much like a shark tooth, but it looks like it’s weathered and worn. It may have spent a good deal of time buried below the sand, being slowly ground by the sand and tide action. Eventually it became dislodged and after a while of being worn smoother it washed up and you found it.


Spatial allocation of road emissions and unit conversion

You have got your emissions over a square kilometer. To get to square meters you divide by 1000000 since $1km^2=10^6m^2$.


Epicenter of earthquake at sea - is it a point on ocean surface or sea bed?

Technically the depth is distance below the ocean floor, but bear in mind that unless there happens to be a cluster of seismometers close to the earthquake (unlikely in the ocean environment), the accuracy of depth estimates is only approximate. If you look at the table of earthquake depths in, for example, the Tonga Trench, you will see a few really deep earthquakes and many times more 'shallow earthquakes' at a nominal depth of 10 km - meaning 'shallow-ish'.


Is this graph showing the average temperature on Earth going back millions of years accurate?

In a word, no.

The graph is based on work by Chris Scotese. The climate science site RealClimate.org has this to say about it:

  Scotese is an expert in reconstructions of continental positions through time and in creating his ‘temperature reconstruction’ he is basically following an old-fashioned idea (best exemplified by Frakes et al’s 1992 textbook) that the planet has two long-term stable equilibria (‘warm’ or ‘cool’) which it has oscillated between over geologic history. This kind of heuristic reconstruction comes from the qualitative geological record which gives indications of glaciations and hothouses, but is not really adequate for quantitative reconstructions of global mean temperatures. Over the last few decades, much better geochemical proxy compilations with better dating have appeared (for instance, Royer et al (2004)) and the idea that there are only two long-term climate states has long fallen by the wayside.

and

  Some better examples of long term climate change graphics do exist. This one from Veizer et al (2000) for instance (as rendered by Robert Rohde):

For a more detailed discussion of graphical reconstructions, see this RealClimate entry


How will axial precession affect the weather in the northern hemisphere (UK latitudes)?

You may be better off getting a more precise answer by cross-posting this physics if you want a precise mathematical explanation of the model.

First, axial precession, as mentioned above, is different than axial tilt. Both change over time, but on different time scales.

That said, the answer is it has most certainly been explored: Milankovitch cycles are an attempt to explain patterns of solar forcing on climate. The wikipedia article does a fairly good job of laying out the foundation of the theory, which is that there are three key components influencing solar forcing: eccentricity (how elliptical our orbit is), axial tilt (the change in amount of tilt of the earth), and axial precession (the actual change of direction of the poles). All three components operate on different time cycles.

Unfortunately for us, the nice Milankovitch theory leads to a huge conundrum: the predictions don't play out as expected. Physics dictates that eccentricity would have the weakest effect of the three, but the largest fluctuations are seen in roughly 100ky intervals (tilt and precession operate on roughly 25k and 50k intervals, eccentricity roughly 100k). This leads to the 100,000 year problem.

Now, all of this is in the context of climate on the global scale, but the point applies just as well: nobody has a conclusive answer as to what exactly is going on, hence the entire field of paleoclimate. Unfortunately for scientists, the Earth is far from a simple rock: our physical (ocean, atmosphere) and biological systems are so complex that it is often too simplistic to pin down changes on one particular phenomenon on a geological time scale. People can study the temperatures  of individual sites over time, but determining the big picture cause of those fluctuations is tough enough for the planet as a whole, much less for a location as specific as the Salisbury Plain.


How should I start studying about 'successful transitions in post-drought economies'?

There isn't a lot written on this yet. A search of FAO, UNESCO, World Bank and Asia Development Bank should turn up answers for specific regions. Keep in mind that climatic modelling is generally quite accurate for temperature, but woefully inaccurate in terms of regional water resources assessment. One should rely as much upon instrumental evidence-based historic trends as upon global climatic models and their down-scaled derivatives. In terms of temperature the important issue is to develop higher temperature tolerant crop cultivars, and maybe totally new crops. For example, fruit orchards that require so many days of frost will not do well under global warming, but new genetically engineered crops might do spectacularly better. The main issue is increased crop-water demand combined with diminishing irrigation water availability. In this, improving the irrigation efficiency is crucial, and requires massive reforms in almost every country. Apart from rising sea level, efficient irrigation combined with an upgrade to modern farming practices is by far the most important climate change adaptation.


Circum polar regions in Interglacial periods

I understand following comment as part of your question:

  Was that climate and terrain habitable by prehistoric humans?

I also assume that by "interglacial periods" you don't mean the last and current one, the Holocene. The next latest one is Eemian. The wikipedia sais on the sea level during that period:

  Sea level at peak was probably 4 to 6m (13 to 20 feet) higher than
  today...

  A 2007 study found evidence that the Greenland ice core site Dye 3 was
  glaciated during the Eemian,[7] which implies that Greenland could
  have contributed at most 2 m (6.6 ft) to sea level rise.[8][9]
  Scandinavia was an island due to the inundation of vast areas of
  northern Europe and the West Siberian Plain.

So there regions were submerged either under ice or sea and definitely not habitable.

And what before? I didn't find as thorough data through a brief google search, but supposing that Homo Heidelbergensis was the first "prehistoric human" species, we have some 600 ky of history, with several interglatials. Continental drift is marginal during this period (few kilometers IS marginal), so no continent could have been near the North Pole during any period of that time. During interglacials, the temperature was about the same as now, +/- 2 °C, and so the sea level was similar to current level. Even if some parts of Lomonosov ridge (just under North Pole) were above sea, they wouldn't be habitable for the humans of that time, at least due to ice. Paleolithic Eskymos in that areas are extremely unlikely.


Why is WRF-Chem not reading my emissions data?

I could run model, i just had to change a bit the namelist. I added three parameters:io_form_auxinput5 = 2,auxinput5_inname = 'wrfchemi_d$<domain>$_$<date>$'  and frames_per_auxinput5                = 168. (168 hours of emissions and hours of simulation)

&time_control
 run_days                            = 0,
 run_hours                           = 0,
 run_minutes                         = 0,
 run_seconds                         = 0,
 start_year                          = 2014,
 start_month                         = 08,
 start_day                           = 04,
 start_hour                          = 00,
 start_minute                        = 00,
 start_second                        = 00,
 end_year                            = 2014,
 end_month                           = 08,
 end_day                             = 11,
 end_hour                            = 00,
 end_minute                          = 00,
 end_second                          = 00,
 interval_seconds                    = 21600
 input_from_file                     = .true.,.true.,.true.,
 history_interval                    = 60,
 history_outname                     = "/scr4/sibarra/masp/run2/1km/wrfout_d<domain>_<date>"
 frames_per_outfile                  = 5000,
 restart                             = .false.,
 restart_interval                    = 10080,
 auxinput5_interval_m                = 60,
 io_form_auxinput2                   = 2,
 io_form_auxinput5                   = 2,
 auxinput5_inname                    = 'wrfchemi_d<domain>_<date>'
 frames_per_auxinput5                = 168,
 io_form_history                     = 2
 io_form_restart                     = 2
 io_form_input                       = 2
 io_form_boundary                    = 2
 debug_level                         = 100
 /

 &domains
 time_step                           = 6,
 time_step_fract_num                 = 0,
 time_step_fract_den                 = 1,
 max_dom                             = 1,
 e_we                                = 191,
 e_sn                                = 181,
 e_vert                              = 35,
 p_top_requested                     = 5000,
 num_metgrid_levels                  = 27,
 vert_refine_fact                    = 1,
 num_metgrid_soil_levels             = 4,
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 j_parent_start                      = 59,
 parent_grid_ratio                   = 5,
 parent_time_step_ratio              = 5,
 feedback                            = 1,
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 /

 &physics
 mp_physics                          = 2,
 ra_lw_physics                       = 1,
 ra_sw_physics                       = 2,
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 sf_sfclay_physics                   = 1,
 sf_surface_physics                  = 2,
 bl_pbl_physics                      = 1,
 bldt                                = 0,
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 num_soil_layers                     = 4,
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 sf_urban_physics                    = 1,
 /

 &fdda
 /

 &dynamics
 w_damping                           = 0,
 diff_opt                            = 1,
 km_opt                              = 4,
 diff_6th_opt                        = 0,
 diff_6th_factor                     = 0.12,
 base_temp                           = 290.
 damp_opt                            = 0,
 zdamp                               = 5000.,
 dampcoef                            = 0.2
 khdif                               = 0,
 kvdif                               = 0,
 non_hydrostatic                     = .true., .true., .true.,
 moist_adv_opt                       = 1,
 scalar_adv_opt                      = 1,
 /

 &bdy_control
 spec_bdy_width                      = 5,
 spec_zone                           = 1,
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 specified                           = .true., .false.,.false.,
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 /

 &grib2
 /

 &chem
 kemit                               = 1,
 chem_opt                            = 1,
 bioemdt                             = 15,
 photdt                              = 15,
 chemdt                              = 2.,
 io_style_emissions                  = 2,
 emiss_inpt_opt                      = 1,
 emiss_opt                           = 2,
 chem_in_opt                         = 1,
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! opt_pars_out                       = 0,
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 /

 &namelist_quilt
 nio_tasks_per_group = 0,
 nio_groups = 1,
/


There is no traceable uncertainty associated with each datum in the ERA-Interim reanalysis.

However, in ERA-5 there is an ensemble.  This multiplies the data volume, but it means you can use the spread between the ensemble members to get an estimate.  See ERA-5 page.  ECMWF want us to use ERA-5 now anyway.


Inconsistency between topographic survey and Google Earth

It's almost certainly a difference in the UTM zones.

In most zones, the minimum easting value is 160,000 mE and the max is 834,000 mE (at the equator). People sometimes use the 'wrong' zone (i.e. not the zone that the location is actually in) if it's more convenient to use a neighbouring zone for some reason. For instance, maybe I have coordinates for 100 points but a few are in a neighbouring zone; it's probably easier to tolerate the location inaccuracy than to reference them to a different UTM zone.

The other thing that can create discrepancies is different datums (e.g. WGS84 in Google Earth, and some other probably local datum for your survey), but that's not what's going on here.

To sum up, it looks like the coordinates you were given are referenced to Zone 19K, to the east of Zone 18K, so the easting is smaller than the usual minimum. The correctly referenced Zone 18K coordinate is the one in Google Earth.

Last thing: I just want to connect with this related question.

EDIT: Conversion

Converting to the correct coordinates is a little risky without knowing exactly how the original coordinates were arrived at. But if they were indeed simply given in UTM Zone 19, then a round-trip conversion via lat-lon shows that they are equivalent to 781493.139 mE, 8177491.483 mN in UTM Zone 18.

We can confirm this with PyProj using the zones' EPSG codes:

>>> import pyproj
>>> x, y = 140800, 8176337  # Zone 19S we think
>>> utm18_wgs84 = pp.Proj(init='epsg:32718')
>>> utm19_wgs84 = pp.Proj(init='epsg:32719')
>>> pyproj.transform(utm19_wgs84, utm18_wgs84, x, y)
(781493.1384182497, 8177491.482738381)

So, if that's what originally happened, these new coordinates are what you want.


Why is the concept of "mass extinction" a relatively recent development in geology?

The idea of mass extinction is not that recent actually: Cuvier (1798), Buckland (1823) and d'Orbigny (1851) for instance were already talking about global catastrophes in earth history, linked to extinctions. But during the same period, Brocchi (1814) and Lyell (1832) proposed that extinctions of species occurred individually and were a gradual process (either only linked to an intrinsic taxa longevity for Brocchi, or variations in the environment for Lyell). Darwin, following Lyell, also thought that extinctions were gradual and not catastrophic. He also noted the fact that hiatuses in the fossil record or artificial concentration in some strata could show apparent extinction event.

The issue with mass extinction is that to demonstrate their existence you need to be able to demonstrate extinction synchronicity and quantify the amount of species going extinct (to show that it is more than just background noise).

Demonstrating the synchronicity of one mass extinction is what Alvarez et al. 1980 managed to do thanks to the Iridium layer at the K/Pg boundary. More generally, the possibility of correlating extinctions precisely is something that evolved in par with the evolution of stratigraphic tools, and the 1970-1980s is the period during which high-resolution stratigraphic methods arose (chronostratigraphy, magnetostratigraphy, stable isotope stratigraphy for instance).

Quantifying mass extinction is what Jack Sepkoski did with his compendium of marine invertebrates (see Sepkoski 1978, 1979; Raup & Sepkoski 1982, etc.). Today, the PbDb (PaleoBiology DataBase) is the project which focusses on that specific issue (see for instance Alroy et al. 2001). It still remains today the main hurdle in studying mass extinctions.

Alroy, J. et al., 2001. Effects of sampling standardization on estimates of Phanerozoic marine diversification. PNAS, 98(11): 6261-6266.
Alvarez, L. W. et al., 1980. Extraterrestrial Cause for the Cretaceous-Tertiary Extinction. Science, 208: 1095-1108.
Brocchi, 1814. Conchiologia fossile subapennina, con osservazioni geologische sugli Apennini e sul suolo adiacente. Stamperia reale. (link to 1843 reedition)
Buckland, 1823. Reliquiae Diluvianae or, Observations on the Organic Remains attesting the Action of a Universal Deluge. Murray, London.
Cuvier, G., 1798. Mémoire sur les espèces d'éléphans vivants et fossiles. Mémoires de l'institut national des sciences et des lettres, 2: 1-32.
d'Orbigny, A. 1851. Cours élémentaires de paléontologie et de géologie stratigraphique. Masson, Paris.
Lyell, C. 1832. Principles of Geology. Murray, London.
Raup D. M. & Sepkoski J. J. Jr, 1982. Mass Extinctions in the Marine Fossil Record. Science, 215: 1501-1503.
Sepkoski J. J. Jr, 1978. A kinetic model of Phanerozoic taxonomic diversity I. Analysis of marine orders. Paleobiology, 4: 223-251.
Sepkoski J. J. Jr, 1979. A kinetic model of Phanerozoic taxonomic diversity II. Early Phanerozoic families and multiple equilibria. Paleobiology, 5: 223-251.


Are the storm cones introduced by Robert Fitzroy still in use?

A page in the book Come Rain or Shine, A Weather Miscellany states that the cones were discontinued on June 1, 1984, due to their being superseded by radio broadcasts and other methods.


Are these the net radiation at surface, and the complete energy balance at surface?

Yes, these are the correct terms.  The first equation is generally termed the 'surface radiation budget' and the second the 'surface energy budget'.

As @Deditos pointed out, additional terms for biological storage and flux can be added and are in fact important sources when considering a microclimate, and these would generally be placed into the surface energy budget.

Another form of the first equation is

Rn = ϵLW↓ − σϵT_s^4 + (1−α)SW↓

Where ϵ is the longwave emissivity of the surface, σ is the Stephan-Boltzman constant, T_s is the surface temperature, and α is the albedo (see Huber et al., section 3.2).

In certain circumstances, it is useful to set them equal to each other by solving for Rn, so you may also see

ϵLW↓ − σϵT_s^4 + (1−α)SW↓ = G + H + LE + (S + B)

Where B is the biological flux.


What are the differences between implicit and explicit methods when applied to modelling Earth systems?

The answer by @IsopycnalOscillation explains some implications of using explicit and implicit schemes and in what applications one is preferred over the other. Here, I describe how these two methods actually work, and how does the implicit scheme uses knowledge of the later state.

Take the ordinary differential equation:

$$
\dfrac{\partial u}{\partial t} = f(u,t)
$$

and for simplicity, let $f(u,t) = -cu$, where $c$ is a positive constant and $u = u(t)$. The equation is then:

$$
\dfrac{\partial u}{\partial t} = -cu
$$

often called the linear drag equation.

Again, for simplicity, we consider the simplest two schemes, a first order forward Euler differencing (explicit), and a first order Euler backward differencing (implicit). As you know, forward Euler differencing yieds:

$$
u_{n+1}=u_n-c\Delta tu_{n}=u_n(1-c\Delta t)
$$

This method is called explicit because the future state is evaluated as function of the present state. From the von Neumann stability method where the growth factor is defined as:

$$
\lambda = \dfrac{u_{n+1}}{u_n} = 1-c\Delta t,
$$

the scheme is stable when $|\lambda|<1$, neutral when $|\lambda|=1$, and unstable when $|\lambda|>1$. We see that the forward Euler method applied to the linear drag equation may be unstable.

With the implicit Euler backward differencing, we have:

$$
u_{n+1}=u_n-c\Delta t u_{n+1}
$$

Rearranging the terms leads us to:

$$
u_{n+1} = \dfrac{u_n}{1+c\Delta t}
$$

Notice that the $u_{n+1}$ is not found on the right-hand side anymore. Here, the growth factor is:

$$
\lambda = \dfrac{1}{1+c\Delta t}
$$

We see that, in this case, $|\lambda|<1$ holds for any value of $c$ and $\Delta t$.

Thus, if one is concerned about stability, an implicit method is advantageous for solving this equation. However, notice that the implicit method is more computationally heavy, even in this simple case, because a division is significantly more expensive operation than multiplication. This is especially true in larger and more complex systems of equations where $f(u,t)$ may be a large matrix that need to be inverted.

A good introductory text on these methods and there applications in atmosphere and ocean modeling can be found here: Mesinger and Arakawa: Numerical Methods Used in Atmospheric Models (1976). For more references, see the answer to this question.


How much did temperature vary during the Ice Age?

Your article says an ice age was 12F (or about 6C) colder, while other sources quote 10C colder, so there is not complete agreement on how much colder an ice age was compared to the present day. This is not particularly surprising, as there were no meteorologists making careful records in those days. Temperatures for ancient times have to be measured in very indirect and roundabout ways, for example by measuring the ratio of deuterium to hydrogen in ice cores, and there are other vaguely similar ways, all subject to error.

All that is necessary to develop an ice age is that more snow and ice should form during the winter than can be melted away during the summer. This doesn't require a dramatic fall in temperature, and if it continues for long enough you will have an ice age. After a while, of course, the increased area of snow will reflect more of the sun's heat back into space, thus increasing the extent of the ice and the amount of heat reflected away in a sort of feedback mechanism.


How long does it take for the atmosphere to mix?

The time scale of interhemispheric tropospheric transport is in the order of one year (Chapter 4 of the book Introduction to Atmospheric Chemistry written by Daniel J Jacob). This is not an exact answer to your question, but might be a fair enough number for the problem you have in mind.


Why do deviated wells show poor correlation to seismic data?

Two-way time to depth calibration is a vertical problem.

How you handle deviated wells probably depends a bit on how you are tying the wells. Here are two things to watch out for:

You should be tying to true vertical depth (TVD) anyway — make sure you're not using measured depth somehow. I expect you are using TVD — so the deviated section will be distorted (squished). It's gong to be difficult to tie, and the modeled waveform won't make much sense.
Sometimes software tries to help you by placing wells at the midpoint between the top- and bottom-hole locations. You don't want this — make sure the well is at the top-hole location, or at the midpoint between the top-hole and kick-off point.

Because of these two things, you may want to ignore deviated wells in the tying effort, if that's an option. If it isn't, focus on tying the straight hole.

Ultimately, you'll want to build a 3D velocity model, using horizons to interpolate the velocity field, and maybe even using other velocities (like stacking velocities) to 'fill in' where there are few wells. At that point you can position everything properly, deviated or not.


Your question is similar to this one: Deciphering NCDC data

In the comments for that question I posted the following two comments which will most likely answer your questions:

  regarding the usage of tenths of a degree, it may be a continuation of an ancient data format from the early era of computers. Removing decimal points changes numbers to integers. In old computers memory capacity & calculating speed was an issue. In binary digits, integers are half the size of floating point numbers & a quarter the size of double precision numbers. Hence the use of integers speeds up calculations & requires less memory (disk drives, tape & RAM) than floating point numbers. In the old days, tape was used heavily as an external storage medium

  There is logic in maintaining the old data format for consistency. If the data format was changed then all the old data would need to be changed & checked to ensure there were no errors. If the format was changed as of a certain date but the old data remained in the old format, people would need to be mindful of the change & when it occurred. Analysis apps would need to first check if the data was from before or after the date of change, condition the data accordingly before doing the calculations. It's easier to keep the old format


How can oil can escape from a shale source rock into a reservoir rock?

Shale typically has a low permeability but shale is not technically impermeable.
Natural occurring faults, fractures and joints in shale can greatly increase the permeability of the rock.  This is why hydraulic fracturing (or fracking) is used to increase fluid flow (for both water, natural gas and petroleum wells) in host rock with low permeability.

Fractured shale can allow a significant amount of fluid because some shale can have porosity values greater than 10%.  Given the grain size of shale, this can be a significant amount of space available to hold fluids.

References:

Physical Characteristics of Fractures and Fracture Patterns
FRACTURED RESERVOIR BASICS
Hydraulic Fracturing
Shale porosity


What would be the effect of bringing seawater pipes to the Sahara desert?

This is an interesting question & I've been waiting to see what answers, if any, would be written.

One affect of creating such a lake would be a localized increase in humidity in the vicinity of the new lake, but nothing that would significantly increase rainfall.

By bringing in salty sea water there could be a risk of contaminating existing underground fresh water stores with salt, making such sources of water unusable.

Also, by being salty, the water could not be used irrigate crops. It may be possible to establish a fishery in the lake, bringing in salt water species of fish, but that would need to be carefully managed.

If for whatever reason, the supply of water was not maintained in the lake, eventually the lake would dry up leaving salt behind. This would then be blow around by the wind, contaminating the nearby land with salt and killing plants on that land and making the land unsuitable for any crops that may grow there now.

Also, if dew were to form, there is no control over how much dew would be created. Many factors come into play when dew forms: atmospheric pressure, wet and dry bulb temperatures, the amount of moisture in the air (humidity) and enthalpy of water vapour and liquid water.

Edit

Having thought about this some more, something similar to what you are proposing has already been done in Egypt, when the Suez Canal was completed and opened in 1869.

Within the canal system is Great Bitter Lake, which is a sea water lake. The presence of such a body of water for nearly 150 years has not increased the amount of moisture in the vicinity of the lake that would allow lush vegetation to grow.


How to reduce the atmospheric temperature artificially?

Ozone does not "filter" UV, it absorbs UV radiation (and undergoes some photo-chemistry reactions in the process).  By absorbing those wavelengths it prevents them from reaching us at the surface, but cause a temperature rise in the stratosphere.

We have plenty of gasses that behave this way for terrestrial radiation (however without the photo-chemical reactions).  Some of these gases are $\mathrm{CH_4}$, $\mathrm{CO_2}$ and $\mathrm{H_2 O}$, which we term greenhouse gases for their ability to readily absorb and radiate at terrestrial (infrared) wavelengths.  If concentrations are high enough, they would reduce some of the infrared spectrum from the sun reaching the surface, but they would also reduce terrestrial emissions into space.  Wherever these gases are doing the absorption one would also experience warming.

If you really want to reduce the global temperature solely through radiative forcing, the answer isn't to find and use a gas to absorb infrared radiation.  The answer is to reduce the concentrations of greenhouse gases in the region you are concerned with.  This will not greatly effect the incoming solar radiation, but it will allow greater radiative losses from the earth and atmosphere (lost to space), resulting in cooling.

You mention that "In my knowledge these infrared rays are the cause the heat".  While infrared radiation will make you hot (e.g. when the sun shines on your skin), the peak emission from the sun is in the visible spectrum and the primary energy source is in wavelengths shorter than infrared.  This shortwave radiation heats the surface of the Earth which in turn conducts heat to the atmosphere and radiates in the infrared.  Without the presence of greenhouse gases, our planet as is (current albedo, etc) would have a global mean temperature around 255 K, which is quite a bit colder than what we experience with greenhouse gases.  If you want to reduce temperatures, regionally or globally with a gas, your answer is to remove some of them, not add to them.


Weather stations and airports around the world almost always include an anemometer. Because of ground friction, the wind speed varies with height, so instrumental deployment is set at a standard height of 10 metres in open rural areas. This may require some adjustment in urban or forested areas. In fact, measuring accurate wind speeds above tree canopies is particularly difficult. There is quite an art in correctly positioning the instrumentation.
Thousands of such anemometers and direction sensors are deployed on nearly every continent, and the results are part of the routine reporting. Such reporting may be every 24, 12, or 6 hours, or even shorter periods down to and including 'continuous'. Then comes the number crunching, in which the strength and direction is gridded and plotted on what appears to be a uniform distribution of points. In reality it is very far from a uniform distribution.

With the introduction of increasing numbers of wind farms higher elevation wind measurements are becoming more important.


This is a large glacier calving event. Calving occur at the front of the glacier, probably a very wet (and likely warm based) glacier where a considerable amount of water is flowing out of the system (thus from the glacier to the sea). The amount of water is important as it can accelerate the ice flow speed on the valley floor, enabling the breaking of big ice chunks along the way. This is what typical calving events looks like: https://pubs.usgs.gov/of/2004/1216/c/images/calving1.gif

When the glacier ice is moving fast in a valley, and encounter hard rock outcrops on the valley floor followed by sudden drops in topography the ice will break, making it what is known as an ice fall: https://pubs.usgs.gov/of/2004/1216/i/images/icefall1.gif , or if the topography is moderate, seracs instead https://pubs.usgs.gov/of/2004/1216/s/images/serac1.gif .

But typical events of normal glacier dynamics as aforementioned does only answer a part of the question. What happened in these picture is that the glacier chunks went seaborne, in a fjord, after a big calving event. The fjord prevent the glacier fragments to spread, forcing these to move along the fjord. This next picture as reported by ABC news show a big calving event that happened in Greenland in 2018, similar to the picture you are showing:

(Source and caption: Tabular icebergs float in the Sermilik Fjord after a large calving event at the Helheim glacier near Tasiilaq, Greenland, June 23, 2018. Source: ABC News https://abcnews.go.com/International/watch-billions-tons-ice-collapse-climate-change-impacting/story?id=57972318)

Here we see the perspective of the rock walls on both sides preventing the spreading and concentrating the glacier chunks.


Can Cirrocumulus and Cirrostratus appear at the same time?

Clouds don't really form in a fully stable atmosphere, so the distinction between "stable" and "unstable" is not real here. An approaching warm front in particular offers many types of clouds, and among other types one may well see high-altitude cirrus, cirrocumulus and cirrostratus clouds appearing together.
Beach weather approaching ... but first:
Wikipedia  gives the following description of the wide variety of clouds that may appear in the case of an approaching warm front.

Cirrus, cirrostratus, altostratus, nimbostratus, then stratus. Other clouds can also often be seen, including cirrocumulus amongst the approaching cirrus, altocumulus with or instead of altostratus (particularly if the front is weak), and occasionally cumulonimbus along with or instead of nimbostratus in summer. Additionally, stratocumulus often appears underneath the main altostratus deck and stratus fractus typically forms in precipitation falling from the thick nimbostratus layer. Often in warm temperatures, rain bearing cumulus congestus clouds can appear under the cirrostratus, and more rarely altocumulus castellanus clouds if convection is sufficient. In cold humid conditions, low airmass stratus or fog may obscure the main frontal clouds.

A picture from the article, showing the major cloud types:

Source: https://commons.m.wikimedia.org/wiki/File:Warm_front.svg#mw-jump-to-license. By WP user Kelvinsong.  License in link.
We see that the cirrus and cirtostratus clouds are adjacent, and from the table entry quoted above that cirrocumulus clouds can appear among the cirrus clouds.  Since in reality some overlap will occur between the different cloud regions, an approaching warm front is likely to produce a region where cirrus, cirrocumulus, and cirristratus clouds can be seen together.


Modified from Cramer et al. 2009 by adjusting to the Gradstein et al. 2012 timescale. In black, it is the Pacific trend; in blue the North Atlantic and in green the South Atlantic.

This graph shows the Cenozoic variations of $\delta\ce{^{18}O}_{Cibicidoides}$ according to Cramer et al. 2009. This proxy is thought to vary according to variations of deep sea temperature (to simplify) and thus should reflect the climate state at a given time. As negative values of $\delta\ce{^{18}O}_{Cibicidoides}$ corresponds to warmer temperature than positive ones, the graph is oriented accordingly. I highlighted the beginning (33.9Ma) and end (23.03Ma) of the Oligocene in red.

The Oligocene starts with a rapid cooling (linked to the East Antarctic glaciation, see for instance Kennett 1977, Zachos et al. 2001, De Conto & Pollard 2003). Yet as you can see on the graph the temperature is still warmer than the late Miocene to Recent period. Some regional trends then show a moderate warming at the end of the Oligocene that culminates in the middle Miocene climatic Optimum, ca. ten million years later.