What tectonic mechanisms cause the North and South Islands of New Zealand to be so geologically different?

New Zealand sits along an oblique convergent plate boundary, where the Pacific and Australia Plates meet. The boundary between the two plates is shown in your second figure as the grey line, with the Pacific Plate to the east and the Australia Plate to the west of it. In terms of relative movement, we can think of the Australia Plate as being stationary and the Pacific Plate having a west to south-west direction of movement. The angle at which the Pacific Plate moves into the Australia plate changes along the length of New Zealand - this is what causes the differences in tectonic style and thus differences in volcanism and earthquake occurance (and motion) between North and South Islands. The angle changes from north to south due to:

a clockwise rotation of the Pacific Plate, and
Pacific and Australia plates meeting at varying angles.

If this boundary was straight and the Pacific Plate moved orthogonal to it, then there would only be subduction, as the Pacific Plate is denser than the Australian. Conversely, if the Pacific Plate moved parallel to the Australia Plate there would be no subduction. Along the length of NZ both of these end-members (and variations in between!) are observed, as not only is the plate boundary slightly curved over the length of New Zealand, but the movement of the Pacific Plate has an component of clockwise rotation. Lamb (2011) shows that there has been 70-90 degrees of clockwise rotation of the boundary/margin over the last 20 million years, and it is still occuring today. The differing angles of intersection between Pacific and Australia plate along the length of NZ cause differences in tectonic style, which give rise to differences in volcanism and earthquake occurance and motion. The figure in this section of the Geology of New Zealand Wikipedia page shows the variations of displacement vector (direction and magnitude of motion) along the length of New Zealand. Notice how the vectors are more orthogonal to the plate boundary in the North Island and become more acute in the South Island.

Subduction occurs in the North Island because the Pacific Plate moves rougly orthogonal to the boundary with the Australian Plate and is denser than than the crust around New Zealand; the Pacific Plate is composed mainly of basalt, while New Zealand is an amalgamation of different sedimentary and volcanic rocks. As the Pacific Plate is subducted is bends and breaks, causing earthquakes. It also melts, causing volcanoes. Notice how the depth of earthquakes in the North Island gets deeper to the west - this is due to the continued westward movement of the Pacific Plate even as it subducts under the Australia Plate.

Over most of the South Island the Pacific Plate moves roughly parallel to the boundary of the Australia Plate. The Southern Alps are caused by slight obduction (up and over movement) of the Pacific Plate over the Australia Plate.

To the south of the South Island the polarity of subduction is reversed, with the Australian Plate being subducted beneath the Pacific Plate  (e.g. Randall, 2007).

This post is a huge simplification of the tectonics of NZ. The geology of this continental fragment is very complicated, but also very interesting! Have a look at this video to see how the plate boundary has changed over time.


Thats a very heavy question and might need a dozen pages to briefly answer, but however its better to summarize the answer and refer to the most detailed review work of Pin Xian Wang and his team entitled "The global monsoon across time scales: Mechanisms and outstanding issues".

I hope this can answer all the questions anyone have in mind. General definition can be that (Global Monsoon (GM) as a planetary scale circulation system. The primary driver of the GM is solar insolation, and the specific features in the underlying surface, such as land-sea distribution, topography, and oceanic circulations, are mainly responsible for the differences among regional monsoon systems.) derived from the above work.

The most important point to be considered that the paper listed above is based on CMIP5 models, however many authors in the region have documented the deviations and differences in all CMIP5 models globally. I will also suggest the work of Alexis Berg for African monsoon system.


What is the difference between vertical turbulent entrainment and turbulent diffusion in the ocean?

I would say that different authors might want to distinguish deep convective mixing with entrainment from purely diffusive or gravity wave-driven mixing. Deep mixing occurs mainly within certain zones (around Antarctica, and in the Labrador sea) because it requires a very strong cooling of the surface to trigger convection. Diffusive or gravity wave-breaking mixing can occur anywhere. Diffusion could include direct processes (like double-diffusion or "salt fingering") or indirect processes such as cabbeling or thermobaricity. If their budgets are based on models, these classes of processes are likely output separately anyway, so it's an easy division to make.

I would break it down as follows, with an eye toward model output:

Vertical advection comes from the advection by resolved or implied vertical velocity. This would include Ekman pumping and any other vertical velocity you might get from an omega-equation.
Entrainment-detrainment comes from convective processes. These require special handling, since many ocean models until recently would not resolve these processes. (They might occur in areas that are only a few kilometers in width, far below the lateral resolution.) These processes can mix together entire parts of the water column at once and often do not care about vertical gradients; they are "non-local".
Diffusive processes are very local/small-scale and depend on vertical gradients of heat and salt. Cabbeling, thermobaricity, and double diffusion are some examples. I include gravity wave-driven mixing here because although the gravity wave generation process is non-local, when they break they can only mix locally within the water column.

This kind of breakdown makes sense for a global ocean model, where these three things are handled by three separate modules. For super-hi-res regional models and non-hydrostatic models, advection and convection start to merge together, and if the model is truly eddy-resolving, entrainment becomes a mix of advection followed by diffusion. How you budget these processes depends on the spatial and temporal scales of the model.


Epicenter location of the 900-930 A.D.,7.4 Magnitude Seattle Earthquake?

If the quake you describe was on the Seattle Fault, you can probably guess the start point and the end point of the fault movement based just on the magnitude of the quake.  (Quakes that "snap" longer portions of the fault release more energy.)  The start point will give you the epicenter location.


First don't rotate the whole thing, you are just confusing yourself by rotating the bedding plane. You can't rotate the bedding planes and expect faults to match up like that. Your eye is treating the bedding plane as horizontal which makes it a completely different from of fault.
A thrust fault is just a reverse fault with a shallow fault angle.

Pay attention to how the bedding planes move relative to each other notice how they begin to double up in both forms of reverse fault, while in a normal fault sections begin to disappear in the middle, being lost from hypothetical drill core entirely. In the reverse faults if you drilled down through the blocks there are areas where you would drill through the dark central bed twice while in the normal there are places where you would never hit it at all.


What are the possible mechanisms that could cause a rapid drop of temperature over few days?

If you look at the first picture, it is the barometric (atmospheric pressure) map of the Honk Kong region for 12 October 2015, from the Hong Kong observatory website. Barometric maps don't change radically over the a period of a few days. The second picture is a similar map for the south east Asia region as at 8 am on 11 October 2015. It is from the same web page as the first picture, I just selected an earlier date from the upper right of the page.

Some things to note about the first picture:

Temperatures in Beijing, Seoul and Hong Kong are 12, 10 and 21
degrees Celsius respectively. It's much colder in Beijing and Seoul than it is in Hong Kong.
There is a high pressure system to the north of Hong Kong and to the
west of Shanghai which appears is part of a larger high pressure system WNW of Beijing. There also appears to be a low pressure system to the
north of Japan (which is confirmed in the second picture).

In the northern hemisphere, air flows clockwise around high pressure systems and counter-clockwise around low pressure systems.

The affect of the high pressure systems over China combined with the low pressure system to the north of Japan is to funnel cold air from the northern latitudes to the southern latitudes. This brought a significant amount of cold air to Hong Kong which caused the temperature to drop.

If you look at Manila, in the Philippines, it is a relatively short distance from Hong Kong. It's temperature is 27 degrees Celsius compared to 21 for Hong Kong.

If you look at the first picture, there is a low pressure system just to the west of the Philippines. With air moving counter-clockwise around low pressure systems, warm air from the equatorial regions is being drawn northwards along the western parts of the Philippines maintaining a very warm temperature in Manila.

Currently, Hong Kong and Manila are being influenced by different weather system and that's why there is a significant difference in the temperatures between the two locations despite their close proximity.

Barometric Map of Hong Kong for 12 October 2015

Barometric Map of South East Asia as at 0800 11 October 2015


Interpolate Gaussian grids to regular fixed grids using bilinear interpolation?

climate data operators (CDO)

define grid

We define a lat-lon target grid with 1°x1° grid cell size 30x30 grid cells starting at 40°N and -10°E (=10°W):

gridtype = lonlat
xsize    =  30
ysize    =  30
xfirst   = -10
xinc     =   1
yfirst   =  40
yinc     =   1

This text is written into a text file. See section 1.3.2 CDO Manual for details and further examples.

If you already have a netCDF file which has data on your target grid, you can also extract the grid definition from that file:

cdo griddes FILE_WITH_TARGET_GRID.nc > myGridDef

myGridDef is a text file.

interpolate data

I assume that we do a bilinear interpolation. This is done with the remapbil operator via:

cdo remapbil,myGridDef INPUT_FILE.nc OUTPUT_FILE.nc

The grid of your input data needs to be properly defined in your input file (that is often a problem).

'properly defined' grid

Alternative 1

The spatial dimensions of the data variable (SST in the example below) need to be named lon and lat. Additionally, coordinate variables lon and lat have to exist (time independent!). These need to have correct coordinate variable attributes (see example below).

Example:

dimensions:
    lon = 30 ;
    lat = 30 ;
    time = UNLIMITED ; // (24 currently)
variables:
    double lon(lon) ;
            lon:standard_name = "longitude" ;
            lon:long_name = "longitude" ;
            lon:units = "degrees_east" ;
            lon:axis = "X" ;
    double lat(lat) ;
            lat:standard_name = "latitude" ;
            lat:long_name = "latitude" ;
            lat:units = "degrees_north" ;
            lat:axis = "Y" ;
    double time(time) ;
            time:standard_name = "time" ;
            time:long_name = "time" ;
            time:units = "seconds since 1900-01-01 00:00:00" ;
            time:calendar = "standard" ;
            time:axis = "T" ;
    float SST(time, lat, lon) ;
            SST:long_name = "sea_surface_temperature" ;
            SST:units = "degree celsius" ;
            SST:_FillValue = NaNf ;
            SST:missing_value = NaNf ;
            SST:var_desc = "sea surface temperature" ;

Alternative 2

The spatial dimensions and corresponding variables exist in the source file but are not denoted as lon and lat. Then each data variable (SST, in this example) needs to have an attribute coordinates that holds the names of the coordinate variables.

SST:coordinates = "lon lat" ;

Example:

dimensions:
    TSTEP = UNLIMITED ; // (24 currently)
    COL = 112 ;
    ROW = 106 ;
variables:                     " ;
    double lon(ROW, COL) ;
        lon:standard_name = "longitude" ;
        lon:long_name = "longitude coordinate" ;
        lon:units = "degrees_east" ;
        lon:_CoordinateAxisType = "Lon" ;
    double lat(ROW, COL) ;
        lat:standard_name = "latitude" ;
        lat:long_name = "latitude coordinate" ;
        lat:units = "degrees_north" ;
        lat:_CoordinateAxisType = "Lat" ;
    double time(TSTEP) ;
        time:standard_name = "time" ;
        time:long_name = "time" ;
        time:units = "seconds since 1900-01-01 00:00:00" ;
        time:calendar = "standard" ;
    float SST(TSTEP, ROW, COL) ;
        SST:long_name = "sea_surface_temperature" ;
        SST:units = "degree celsius" ;
        SST:coordinates = "lon lat" ;
        SST:var_desc = "sea surface temperature" ;

Note:

If the coordinate variables are missing, then they can be created via the setgrid operator. If we want to add a grid definition to the source file then we need the source file's grid definition in the text file (see 'define grid').

cdo setgrid,mySourceGridDef INFILE_NO_COORDS.nc INFILE_WI_COORDS.nc


Given only pressure and potential equivalent temperature, can I determine other variables?

I think you cannot, might be wrong on that though.

In a single-fluid atmosphere without condensibles, the pressure is essentially proportional to $P \sim \rho \, T$ and potential temperature is essentially $\theta \sim T
P^{1-1/\gamma}$, with the temperature $T$, and the adiabatic index of the gas $\gamma$.
So in this situation you could reconstruct density and temperature from $P$ and $\theta$, but if you add another fluid into the mix (which is your condensible), then I don't see how that should be possible.


Why the "Mission to Earth’s core" proposal suggests using liquid iron instead of lead?

I've already commented on this before here. This will not work regardless of whether this is iron or lead or anything else. The fact that it was published in Nature does not mean it is true, always remember that.

A 108 kg ball of iron would have about 30 metres in diameter, which is not a lot, compared to Earth scales.

Here is why it will not work:

We have very massive objects on the surface. They are called buildings. They don't sink. Even though they are not that dense, buildings stand on foundations that funnel their mass to a small area, creating very large pressures. The point of the foundations is to hold the building on solid rock so they do not sink.
The author suggests to initiate the crack with explosives. Perhaps a nuclear explosion. We had many nuclear explosions in the past - they don't make cracks. They make craters.
Crack propagation is only relevant in the lithosphere. Once you get to the asthenosphere, about 200 kilometres deep, cracks don't propagate any more and the equations the author presents are meaningless. Plastic behaviour of rocks is achieved much earlier anyway.
Even if there is a crack, it will have smaller cracks radiating from it. The liquid iron, being liquid, will fill those smaller cracks and lose from the mass of the main liquid blob. With time, the blob will get smaller and smaller until it will not be able to do anything any more.
The melting point of iron is more than 1500 °C, this higher than the temperature of the ambient rocks. It will lose heat quicker than it gains by gravitational potential and solidify.
The iron will react with the rocks around it. Particularly if it is liquid, because these things are highly reactive. It will start dissolving things into it (which will counter point 5), but it will also lose iron to the surrounding rocks. It will dissolve oxygen, carbon, sulfur, possibly alkalis like sodium, maybe silicon. These are light elements that will overall lower the density of the ball of iron, bringing it to a stop.
The oxygen fugacity of the crust and upper mantle is higher than iron-wustite, which will be imposed by the equilibrium with iron. Therefore, it will leach oxygen from the surrounding rocks, essentially turning some of the liquid iron into "rock". This will also decrease the mass of the blob of iron.

You were specifically asking about lead. All points above (except 5) are equally applicable to lead.


How does this melted-wax erosion pattern form in sandstone?

Differential erosion, the "wax-drips" have been hardened by chemical deposition, usually by calcium salts, washed down from the soil at the top of the cliff. There are different mineral deposits that can cause this effect including but not limited to calcite, silica, iron, manganese, fine organic matter, alumina, and fine clay, to name a few, anything that can be deposited between the grains of the sand stone will do really. These minerals flow into the sandstone when wet and as the surface dries they act as a cement increasing the durability of the sandstone in the areas that have been wetted by the mineral rich water. The wind then erodes the surface of the rock and because the cemented material is harder it cuts more deeply into the rock between the water pathways.


Is the Arctic sea ice volume increasing?

This question has already been answered on Skeptics.SE:

Did the Arctic Ice Sheet grow by 60% from 2012 to 2013?

The short answer is that those two graphs are consistent with each other. The first shows a 33 year trend of declining Arctic sea ice (summer minimum, I think), whereas the second highlights inter-annual variation in Arctic sea ice over the last four years.  We don't expect long-term trends to be strictly monotonic increases or decreases, so both positive or negative year-on-year differences, such as those between 2012 and 2013, can be consistent with the trend.


What is the difference between GFS analysis and GFS forecast data?

I'm no expert at this, I've just been working with a variety of gfs gribs for a while now.  The only thing I know about them is what I've read and I'll cite them here.  I don't know where you got those files.  It looks like you're looking at the files that are sub areas, but in general the file format goes as shown more clearly on this website:

data/nccf/com/gfs/prod/gfs.YYYYMMDDHH/gfs.tHHz.formidffhour !GRIB_version/ncep/model/#000/YYYYMMDDHHNNFfff/parm/level! ######

Grid #004, 0.5x0.5 degree lat/lon grid, formid = pgrb2 (Hours F000-F180) GRIB_version=grib2 (grid number appears as #000)
Note: fhour: anl, f00, f06, f12....f384 (note: 2 or 3 digit fhour as on ftp server)
GRIB2 Note: "model date" and "forecast hour" fields are not separated by "/"

I suggest you pull your files from here to start with.

The anl vs fxxx is the fxxx are intermediate simulation runs that often contain uninitialized data.

  Forecasts contain the same parameters as analysis AND accumulated forecast variables such as precipitation.  Analysis files, if they contain the forecast-only fields, set them to zero.  For instance, both FNL and GFS files obtained directly from NCEP, contain the CRAIN variable.  But, in the FNL file, all of the CRAIN fields will be zero.

  In summary, the FNL analysis incorporates ~10% more data than the GFS by waiting for ~3 hours longer for the data stragglers.  GFS forecasts contain accumulated fields, such as precipitation, that are missing or set to zero in the analysis fields. - link

This analysis-forecast cycle is complex and iterative.  Here's a bit more detail.

And here is the current status in real time of models running.


Which ocean is going to be swallowed by the Supercontinent Cycle, Atlantic or Pacific?

The pacific has already lost part its spreading center, north america has been pushed over it.. A few tiny non-contiguous plates like the  Juan de fuca plate is all that is left of the east pacific plate. in places like the san-andreas the north american plates has complete overridden the spreading center.

In truth there are four possible predictions for the future: Novopangea, Pangea Ultima, Aurica and Amasia. two predict the atlantic disappearing one predicts the arctic and the final predicts the loss of the pacific. I think the latter two are more likely given what we see in the north america/pacific interaction but this is just opinion, the truth is we don't know a lot about how oceanic plates disappear or how spreading centers die because by their very nature they do not leave much evidence behind.


Have we looked for human-like signals in the geological record?

Any nuclear activity would be visible in the changes of the isotope ratios far longer than the existence of our Universe. Nothing was found until now, except one in Gabon. It was natural phenomenon: at the time, 2billion years ago, there was far more ${}^{235} \rm U$ and the ground water could moderate uranium ore, if it was enough rich.
Except that, nothing was found until now.
Offtopic, but the case is same for radio signals. If any Earth-like civilization would exist in 100 light years away, we would see their radio signals. There is radio silence.


Where on Earth is the magnetic field intensity stronger?

If we consider only the magnetic field generated by natural sources, and not the ones generated by human activities. The general trend is that higher intensities of the magnetic field happen close to the magnetic poles. However, this is just a general trend. The southern hemisphere experiences the highest magnetic fields intensities, reaching over 65,000 nT, and this southern maximum matches fairly well with the south magnetic pole. In contrast, in the northern hemisphere, the maximum happens in Siberia, quite far from the north magnetic pole that is currently very close to the geographic north pole.

There are many models of Earth's magnetic field. You can find a comprehensive list at geomag.org. You can find the intensity output of NOAA's World Margentic Model for 2015, here. Shapefiles with the contours are available for 2018 here (as well as more maps), and the coordinates of north and south poles computed by the WMM model up to 2020 are available here. Putting together all the information for 2018, the map looks like this

The contour interval is 1000 nT, grid lines interval is 10°, and the green dots represent the south and north magnetic poles.

The maximum intensity contour in the northern hemisphere happens in Siberia with a value of 61,000 nT and the center of the contour is located roughly 3,100 km away from the magnetic north pole.

The maximum intensity contour in the southern hemisphere is between Australia and Antarctica, with a value of 67,000 nT and the center of the contour is located roughly 450 km away from the magnetic south pole.

So the answer is: Yes, there are. Maximum field intensity doesn't happen at the magnetic poles. Specially in the Northern hemisphere, the maximum intensity happens in Siberia, a few thousand kilometers away from the magnetic pole. The maximum intensity globally according to NOAA's WMM model is located in the southern hemisphere, approximately at 60° 15' S, 135° 52' E, about 450 km away from the south magnetic pole

The projection in the map above (geographic) generates a big distortion of the distances, the images below show the same information in polar stereographic projection:

Now, it is worth adding that the above information wouldn't be significantly modified if you consider natural magnetic anomalies due to magnetic crustal deposits. This is because magnetic anomalies are usually smaller than a few hundred nT, therefore the magnetic field intensity pattern is largely controlled by the internal magnetic field. Magnetic anomalies are not mapped over the whole world, but the World Digital Magnetic Anomaly Map, provides a pretty good estimate

However, these anomalies are measured  at an altitude of 5 km above mean sea level, and have a resolution of 3-arc-minute resolution (~5 km). Therefore, it doesn't rule out the existence of very strong and localized small scale anomalies, that might lead to large variations on the magnetic field intensity but over a very small area.
The maximum value within the World Digital Magnetic Anomaly Map dataset is 8570.78 nT, but even considering all anomalies, the largest field intensities remain within the same contour near the magnetic south pole.


What are some good resources to learn about geophysics?

There are two universally loved geophysics texts : Turcotte and Shubert  and Stein and Wyessesion. Both are upper division/lower graduate level texts. Fowler is great if you do not have a strong mathematical background (multivariate calculus). Beyond that, it might be difficult to get a a good understanding without an Introductory physics course, and a few semesters of calculus. A good way to learn about geophysics, is in fact, just learning about physics and mathematics in general. Hopefully this stack exchange will also become a good resource.


I've seen data like this before. My research focused on acquiring/processing both vertical and horizontal component data in subsurface environments characterized mainly by saturated and/or unconsolidated facies. So, it's entirely possible even for a that kind of surface-wave to display such low apparent velocities.

What I think you are seeing at that particular offset and traveltime is the Rayleigh-wave beginning to 'disperse' - AKA seismic dispersion beginning to manifests itself as a results of the wavefield convolving with layering and near-surface heterogeneity.

As for a refraction arrival, if it's indeed present, it should be extremely weak and probably only discernible via some kind of gain (e.g. AGC).


Antarctic and arctic meltwater is "bad" because it's dark, but why is transparent liquid on white stuff so dark?

Water has lowest EM absorption in the blue part of light spectrum and increases rapidly  towards both UV and red parts of spectrum.

As a result in visible light water is blue. Same goes for the ice as it has very similar absorption spectrum.

While there is a lot of white in the picture, all of it is a thin snow cover on top of blue ice. Once the snow melts you get a blue water on top of thick layer of transparent blue ice that combined absorb most of the light making ponds looks exceptionally dark. Water would be so much lighter in colour if, for example, instead of transparent ice, you'd have sand on the bottom.


Have fossils ever been found in the asphalt lakes of Trinidad and Venezuela?

Trinidad

Quote from https://www.smithsonianmag.com/travel/trinidad-and-tobago-nature-and-scientific-wonders-18329129/

  Quite an oddity sits in La Brea in South Trinidad—the world's largest asphalt lake. Pitch Lake, while perhaps not the most beautiful of nature's creations, is certainly an intriguing one. The 100-acre lake is believed to be 250 feet deep in the center and contains some 10 million tons of pitch. As the lake replenishes itself and turns over, artifacts both peculiar and historically significant such as a mastodon tooth, fossil remains of the giant sloth, and Amerindian artifacts have been uncovered. Mineral pools on the lake are purported to have healing properties because of their high sulphur content.

Venezuela

One example from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097493/ is an article with the title

  Fossil snakes (Squamata, Serpentes) from the tar pits of Venezuela: taxonomic, palaeoenvironmental, and palaeobiogeographical implications for the North of South America during the Cenozoic/Quaternary boundary

Summary:

yes, fossils have been found


Why do we consider hydrostatic pressure and lithostatic pressure separately?

I think (but I'm not an expert on the topic) that your assumption is wrong! The pore fluid pressure and effective pressure exerted on the rock are not identical. And during log measurements, it is only the pore fluid pressure that is measured, $\sigma_\text{pore}=\rho_\text{water}gz$. As long as the water is connected through pores to the earth's surface and it is assumed to be hydrostatic (not-flowing), the measured pressure in the pores will create a pressure to push up the overlying water only -- the rocks will take care of carrying the overlying rocks themselves! If you'd drill a borehole, the water-filled column would be filled with water exactly to the earth's surface. The effective pressure on the whole system is then $\sigma_\text{effective}=\sigma_\text{lithostatic}-\sigma_\text{pore}$.

However, once a seal is formed (e.g., a shale formation overlies a formation), the pore water suddenly has to also carry the overlying rock formation! The pressure will then increase significantly, and the pore water pressure that you measure becomes more similar to the effective pressure.

The pore water pressure thus increased below a seal because it is stuck. Once you drill a hole from the surface to this formation, you allow water to flow freely to the surface, and it is clear that it is under overpressure with respect to the lithostatic pressure, because it will blow out!

See also this comprehensive set of slides: http://ocw.utm.my/pluginfile.php/1509/mod_resource/content/0/To_upload_OCW_Formation_pressures.pdf .


A US definition is:

  Wind Gust: Is a sudden, brief increase in speed of the wind. According to U.S. weather observing practice, gusts are reported when the peak wind speed reaches at least 16 knots and the variation in wind speed between the peaks and lulls is at least 9 knots. The duration of a gust is usually less than 20 seconds.

So it must be 9 knots above the non-gust background.


What happens when the North and South poles flip?

First of all I will try to explain what a geomagnetic pole reversal is.

A magnetic pole reversal happens when the magnetic field weakens until it can no longer sustain itself. This can take 1000-3000 Years or more to complete the time it takes is very variable.

After years with a weak and sometimes variable magnetic field. The magnetic field re emerges in its original, or in a changed direction. So it is not the poles moving over the surface of earth.

Link https://en.wikipedia.org/wiki/Geomagnetic_reversal

What happens during a geomagnetic pole reversal.

During a pole reversal earths magnetic field is weak so our planet is less protected from cosmic radiation and from the solar wind.

Link https://en.wikipedia.org/wiki/Earth%27s_magnetic_field

This increase in cosmic radiation does not fit any of the known extinction events that have happened in Earth's history. This does not mean nothing at all will happen to life during this time it only means the dangers are low.

If you want to follow how the cosmic radiation changes you can do this on the last part of this page http://spaceweather.com/

Link https://en.wikipedia.org/wiki/Cosmic_ray

I mentioned in my comment Earth's magnetic north pole is really in the south. To understand how this can be true in a magnetic compass the magnetic north pole is repelled by Earth's north pole so it does point in the opposite direction (a bad explanation but it is how it works).


Why is carbon pollution given more importance than Sulfur pollution or Nitrogen pollution?

greenhouse gases other than CO2

The gases N2O and CH4 are relevant greenhouse gases besides CO2. They even have a stronger warming impact per molecule. A brief summary for N2O is provided in its Wikipedia article.

Edited later on (thanks to Communisty for the comment): The global warming impact of these greenhouse gases other than CO2 is measured/presented in CO2-equivalents: i.e. the warming potential of N2O equals 298 CO2-equivalents, which means that one N2O molecule is 298 times worse than a CO2 with respect to its warming potential. Therefore, CO2 is more visible in the media.

Why focus more on CO2?

contribution to global warming

Although N2O and CH4 have a stronger greenhouse impact than CO2 per molecule, the concentrations of them are considerably lower. Therefore, in sum (heat trapping potential x number) CO2 has a stronger impact on climate change. The figure below visualizes it nicely (please find the reference below):

Figure TS.6 on page 53 of the 5th IPCC Assessment on the Physical Science Basis (www.ipcc.ch/report/ar5/wg1/).

practical aspects

There are also practical aspects why a focus is put on CO2: many CO2 emission sources are know and clearly to identify (combustion processes). Moreover, many of the CO2 sources are point sources. Some of them are many and small (cars) but nonetheless: we know them and know to locate them.

In contrast, N2O and CH4 emissions sources are often diffusive sources (or area sources) such as natural and agricultural soils and cattle (and other) life stock. Here you find a text and a nice figure on this topic: www.soest.hawaii.edu/mguidry/Unnamed_Site_2/Chapter%202/Chapter2C3.html

Thus, it is easier to reduce CO2 emission by specific measures: don't combust so much fuel/combustion-material.

greenhouse gases vs. air pollution

As stated above, CO2 (in sum) has a higher global warming impact (in the moment). And large parts of its emissions are easier to manage. Therefore, the focus is on CO2.

Moreover, greenhouse gas emissions have a global impact: the greenhouse gases alter the global climate. In contrast, air pollutants have a local or regional impact. Also their life time or/and residence time in the lower troposphere is shorter than the residence time of greenhouse gases in the atmosphere. Therefore, the temporal and spatial extend of greenhouse gases is larger/longer than that of air pollutants. Thus, one focuses more on the longer lasting global problem: greenhouse gases.

regional vs. global

Because greenhouse gas emissions cause global problems they are tackled on a global level -- IPCC, United Nations Climate Change Conferences, etc.

Although air pollution is also a topic dealt with on global or continental level (e.g. hemispheric transport of air pollutants (HTAP), European Monitoring and Evaluation Programme (EMEP)), it is mainly a regional or local problem.

In Europe many municipalities have recognized the problem of air pollution in cities and try to deal with it (actually, they are forced by the EU). Also in Chinese cities municipalities start working on this topic.


When was the last time there was a landlocked body of water bigger than the Caspian?

The Tethys, which was previously an ocean, became landlocked by the Indian subcontinent to form a massive body of water before the Indian plate collided with the Euarasian to form the Himalayas (about 250-400 Ma).

I don't see any visuals on the Tethys as a sea (as opposed to when it was an ocean) widely disseminated on the web. Here's an approximate diagram from my memory of a textbook:


The rupture velocity of earthquakes is another factor that determines the magnitude of a tsunami.  The term tsunami earthquakes was coined by Hiroo Kanamori in 1972 to describe when an earthquake having a slower rupture velocity (~1km/sec), compared to a more normal velocity (2.5-3.5 km/sec) of a typical earthquake, causes a tsunami greater in magnitude than expected.

This paper investigates the effect of a horizontal movement of sediments due to an 1896 earthquake that may have created a tsunami earthquake.


Before defining wind velocity, I think it is necessary to explain the continuum assumption. The basic idea is that even though gases are composed of discrete molecules occupying a small fraction of the total volume filled by the gas, gas flows are made up of many individual collisions between gas molecules. In most applications, the flow field is assumed to behave as a continuum and the discrete molecular nature of gases is ignored. By doing so, fluid properties (e.g., density, velocity) can be defined anywhere in the flow.

In order for the continuum assumption to be appropriate, the length scale of the application must be much larger than the mean gas particle path length. In most atmospheric conditions, including in anemometers, the mean path length is on the order of micrometers, while the anemometer size is much larger.

While most anemometers (cup, vane, laser, acoustic resonance) measure wind speed some measure the wind's pressure (e.g., tube, plate anemometers). In the case of cup anemometer, like any other measuring device, there is an error associated with the measurement that depends primarily on the cup size. In general, the anemometer factor (the ratio of wind speed to the rotation speed of the cups) has a value between two and a bit more than three and it depends on the size of the cups and arms.


Is there the right term for time points in hourly weather forecast?

I spotted that a scientific paper [1] uses the "lead time" term, and then its definition [2] was found from the Glossary of Meteorology by the American Meteorological Society.

forecast lead time
The length of time between the issuance of a forecast and the
occurrence of the phenomena that were predicted.

[1] https://link.springer.com/article/10.3103/S106837391405001X
[2] http://glossary.ametsoc.org/wiki/Forecast_lead_time


WRF vertical integrated moisture flux using NCL

I am going to explain my answer based on how the authors from this paper calculate this Vertically integrated moisture flux convergence as a predictor of thunderstorms. This is the equation that is defined in their paper

$$ VIMFC = -\frac{1}{g} \int_{700\,hPa}^{1000\,hPa}\ (\frac{\partial u q}{\partial x}+\frac{\partial v q}{\partial y}) * dp $$

For a reference for the vertically integrated moisture flux please look at the equation in the reference A Hydrological Definition of Indian Monsoon Onset and Withdrawal

$$ VIMFT = -\frac{1}{g} \int_{700\,hPa}^{1000\,hPa} (q * u + q * v) * dp$$

So I will provide an algorithm for a numerical implementation of this equation and opinions can vary what exactly constitutes a "atmospheric layer" in NWP.

In this equation q is the specific humidity, u and v are the zonal and meridional velocities, p is the pressure,  g is the acceleration due to gravity.

Moisture flux convergence or divergence is

$$ (\frac{\partial u q}{\partial x}+\frac{\partial v q}{\partial y})$$

Moisture flux is

$$ q * u + q * v  $$

You then need to sum the horizontal moisture flux convergence(or in your case moisture flux) over the following layers
1) 1000 - 925 hPa
2) 925-850 hPa
3) 850-700 hPa
So the definition of what a "layer" is subjective and you can include three pressure surfaces in it if you think a deep layer is required as opposed to a shallow layer. Numerically a layer can be thought as the average values of two pressure surfaces. As an example in your case you take the average of the velocities at the 1000 hPa and 925 hPa surfaces and the average of the specific humidity at the 1000 hPa and 925 hPa.

Once you calculate the horizontal moisture flux(or if you are calculating MFC then you need to use  finite centered differences and backward and forward differences at the grid boundaries and that procedure is explained in this ESSE answer - Moisture flux convergence numerical procedure and here Finite differences for calculating MFC) over each layer you multiply that by dp and then sum up the contributions.

UPDATE

In response to the questions in the comments

$"1000 - 925"$

is not to be interpreted as a subtraction but as an average.

What needs to be done is to take an average of two pressure surfaces in the following manner

$ q_{layer} = (q_{1000} + q_{925})/2$

$ u_{layer} = (u_{1000} + u_{925})/2$

$ v_{layer} = (v_{1000} + v_{925})/2$

Once you do this for every layer multiply by the corresponding value of dp and in this case

$ dp = 1000 -925 $

This is a subtraction. Then you plug the individual values into the sum of individual contributions for each layer and then sum the layers

$ (q_{layer}* u_{layer} + q_{layer}* v_{layer}) * dp$

The units of VIMFC is typically

$$ 10^− 5 \, kg \, m^− 2 \,s^− 1 $$


Why is the level of UVA radiations on the earth surface the same throughout the day, while the level of UVB significantly varies?

UVA rays are very penetrating and are not weakened by the ozone layer, so are much the same throughout the day. UVB rays are of shorter wavelength, much less penetrating, and are weakened by passing through the atmosphere. At different times of day there are thicker or thinner layers of atmosphere to pass through. Near sunset, the UVB rays will be oblique, and therefore have a greater thickness of atmosphere to pass through, so will be consequently weakened. UVC rays are of even shorter wavelength and are mostly blocked by the ozone layer.


Spatial extent and predictability of various hazards.

Drought is not comparable to earthquakes and hurricanes because of the difference in time scale involved. We can generally consider earthquakes and hurricanes to be discrete events at a point in time. Drought, however, has a significant and variable time component. For example, you might consider a year of severely below average rainfall to be a drought but there are also multi-year droughts with prolonged periods of low rainfall. Descriptively, you can plot the average rainfall for an area along with the cumulative deviation from the average to see the trends. Even that should be used with caution - one year of very high rainfall may have a big effect on the deviation from the mean but won't really help rain-fed agriculture in subsequent low years. Here is a guide to the different definitions of drought.

The probability of a hurricane hitting the eastern United States in the next decade probably approaches 1.0 but to narrow that down to specific cities is more difficult. Once one has formed, the meteorologists are getting very good at predicting where it will make landfall. In between, the probability of a hurricane striking a particular city in a given year is low.

With regards to earthquakes, there are many known faults where measurements indicate a high probability for a large earthquake. However, the timing may be within hundreds or thousands of years. We can predict where but not when.

These are all different ways of looking at prediction that illustrate the considerations in how we think about 'prediction'. Ranking them is comparing apples to oranges.


What could be causing microseisms on Mars?

I think that the mechanism suggested in the article fits the bill the best:

  On Earth, microseisms are ubiquitous, caused largely by the sloshing
  of the ocean by storms and tides. Mars, despite the dreams of science
  fiction writers, has no present-day oceans. Instead, this newly
  discovered noise is likely caused by low-frequency pressure waves from
  atmospheric winds that rattle the surface, inducing shallow,
  longer-period waves in the surface, called Rayleigh waves, Lognonné
  said.

See e.g., https://www.lpi.usra.edu/meetings/lpsc2012/pdf/1994.pdf , for a modeling study of the microseismic events on Mars due to wind.


Why exactly did the use of Greek letters as names for Hurricanes happen the first place, despite the fairly obvious and predictable impracticalities?

Well for one major reason, the assumption was that seasons with so numerous hurricanes were rare. Canonically, tropical cyclones typically follow a Poisson Distribution. If you consider that, of the English alphabet, only 21 of 26 letters are used for names, and if you assume that the average number of hurricanes from the past decade were to stay unchanged compared to this decade (155 tropical storms/10 years= 15.5 tropical storms per year), then the probability of going over 21 tropical cyclones is about 6.96%. Such a view is backed up by the World Meteorological Organization.
But if you say, "aren't the number of tropical cyclones supposed to increase under climate change?" to challenge my assumption of constant tropical cyclones per decade, I will argue that canonically, the frequency that tropical cyclones form should decrease, but the intensity should increase. So the assumption doesn't actually hold, but should actually overestimate the probability of more hurricanes in the long term.
So why use Greek letters? Well, to be consistent, something has to be used to draw attention to the storm. Cycling back to the A's sends the wrong message. And the Greek alphabet was used previously, without major consequence.
Arguably, it may be easier to only name storms that make hurricane strength. However, that would be inconsistent, and may miss strong, rain-heavy storms. For example, Tropical Storm Allison was retired without making the hurricane wind requirement.


Is it coincidence that Antarctica is centered on the south pole?

It's an interesting thought.

Antarctica is not so balanced as it might appear on the maps. East Antarctica is old, consisting probably of cratons and Proterozoic orogenic domains whiles West Antarctica is much younger, thinner and have mostly been accreted during the formation of Gondwana (See e.g Boger 2011). If there was a direct control of the location of the continents from the rotation, one would expect East Antarctica to be centered rather than the whole landmass as it appears today, moreover one would expect a similar continent, resting at the North Pole.

However, Antarctica have been relatively stable, at least for the last 200My. The breakup of Gondwana could partly be simplified as one continent after the other radiating away from Antarctica, that mostly stayed in the same latitudes (See e.g Torsvik et al. 2010). Today Antarctica has relatively low tectonic activity and little continental drift, mostly a rotational component.

My personal believe is that astronomic potential fields, as gravitational tidal forces, might become a more important parameter in future for mantle dynamic models and maybe even some tectonic mechanisms, but I might be wrong and studies so far (e.g. this) have not shown good evidences and didn't attract much attention.


Why are there different vertical gradients of temperature in different layers of the atmosphere?

The strong heat flux through the Earth's atmosphere, the presence of greenhouse gases, convection, and mixing conspire to push the troposphere away from thermodynamic equilibrium (isothermal atmosphere) and toward an adiabatic atmosphere. The natural drop in density with altitude means an adiabatic atmosphere is subject to a lapse rate, a nearly linear drop in temperature with increasing altitude.

Temperature rises with altitude in the stratosphere because this is where the ozone layer exists. The troposphere is primarily heated from below. In contrast, the stratosphere is primarily heated from within via absorption of ultraviolet sunlight by oxygen and ozone. A similar situation exists in the thermosphere, which is where the extreme ultraviolet and higher frequencies of sunlight are absorbed.

The thermosphere is where atmospheric temperatures attain their highest values. (BTW, you wouldn't feel those extreme temperatures if you put on a spacesuit and took a spacewalk in the thermosphere. There's hardly anything there that high up.) In between the thermosphere and stratosphere is where atmospheric temperatures attain their lowest values. This is the mesosphere. Most of the extreme wavelength radiation has already been absorbed in higher layers. The mesosphere is too thin to support an oxygen-ozone cycle. All that's left is radiational cooling supported by a strong greenhouse effect, making the top of the mesosphere (the mesopause) extremely cold.


There are cloud identification guides, are there also (guided) rock identification websites?

I looked through your step-by-step cloud identification guides, and they seem pretty easy. The overwhelming criterion for which cloud it is is "how does it look", with the addition of the obvious "is it raining" or "do you hear thunder". After that, you only get 10 different types.

Here's why it would not work for rocks:

"How does it look" is only one criterion out of many for rocks, and it is commonly the most misleading. A granite has many different appearances. Quartzite, limestone, basalt, can all take many forms. Weathering, alteration, fresh surfaces vs broken surfaces, desert patina or stream-polished can all change a rock's appearance completely. Grain size affects colour. Lighting affects colour.
There are many more than 10 types of rocks. Basalt, gabbro, granite, rhyolite, dolomite, limestone, sandstone, shale, diorite, trachyte, ijolite, kimberlite, komatiite, eclogite, blueschist, greenschist, a conglomerate containing all of the above, and the list goes on and on. Some rocks may not even be rocks. Smelting slag, coal, hardened dung, a piece of concrete or any other kind of synthetic construction stone.
The many other tests used to identify rocks may not be applied properly by people with little experience. Is the mark left by your knife on the rock really a scratch on the rock, or is it the streak of the knife? Is it really hard or is the scratch you just did was actually silt sized grains of quartz dislodged? What's "heavy for its size"? Everything is relative. Where a geologist sees green, brown, yellow, black or red, a non-geologist sees brown, brown, brown, brown and brown.
Even professional geologists sometime disagree on what a rock is. I've seen (and been part of) so many arguments about rocks, and I have 7 years of experience in this thing. Occasionally the verdict is given by...
Methods to positively identify a rock (thin sectioning, scanning electron microscopy) that are not available to the general public and probably not even to the amateur geologist.

Therefore, if a key would exist, it would be extremely complicated and pretty much useless.


Help understanding this image of an unusual double reflected rainbow

What you are seeing are a primary rainbow (rainbow #2 in the linked image), its secondary rainbow (#4), a reflection rainbow (#1), and its secondary (#3).

Note well: The linked article called these reflected rainbows, but that is incorrect. A reflected rainbow is yet another atmospheric optical phenomenon that occurs when one sees what appears to be the reflection of a rainbow on water in front of the viewer. The water was behind the photographer in the linked image, and the extra two rainbows were in the sky rather than in the water. (There is no water in the linked image.)

The reflection of the Sun off of the water behind the viewer result in a reflection rainbow in front of the viewer. The center of a primary rainbow is the antisolar point from the perspective of the viewer of the rainbow, which is below the ground. The center of a reflection rainbow is the anthelic point of the reflection of the Sun, which is above the ground. Thus rainbow #1, the primary rainbow from the Sun itself, is lower than rainbow #2, the primary rainbow from the Sun's reflection off the loch behind the photographer. The two rainbows intersect at the ground.

Rainbows can have secondaries, and so can reflection rainbows. These are bows number 4 and 3. Note that the sky and land are darker between the primary and secondary, brighter outside the secondary, and much brighter inside the primary. The lighting outside the secondary is the natural lighting level.

One way of looking at rainbows is that they transfer some of the light that would normally fall between the primary and secondary to inside the primary, and that this transference changes a bit with frequency.


snow modelling using raster data with different resolution

To make sure I'm understanding correctly: You need to reproject some data from one coordinate system to another. Then you need to interpolate or resample it onto a grid.

If you are already familiar with MATLAB, then you can certainly use MATLAB. Look to the Mapping Toolbox for coordinate transforms (although it does not make life easy IMHO), and check out griddedInterpolant and scatteredInterpolant and their options for interpolation.

If you're more tool-agnostic it might be easier in R (check out the spatial, raster, and rgdal packages). Or, depending on your GIS system, there is probably a way to script or automate it there so that you do not have to work through the process manually for all your files.


How to figure the MODIS snow cover against different elevation zones?

The problem (if there is only one) is in the way you are importing the data into .mat file. If with the files you provided I do

data = load('sc_8days.mat') ;
data = data.sc8days ;
[nx,ny,d] = size(data) ;
imagesc(data(:,:,1));

I get this nonsense:

and d=23, while the MOD10C2 data has only four bands: Eight_Day_CMG_Snow_Cover, Eight_Day_CMG_Cloud_Obscured, Eight_Day_CMG_Clear_Index, and Snow_Spatial_QA (see product specifications).

However, if I load the data and plot directly from the .hdf file you shared, with

data = hdfread('MOD10C2.A2000185.006.2016068190452.hdf','Eight_Day_CMG_Snow_Cover');
imagesc(data);

I get:

Which does makes sense and show the actual data.

Therefore, the way you are importing the data into your .mat file is messing things up.

The error is in this line

B2(i,j,1:24) = reshape(Eight_Day_CMG_Snow_Cover{i,j},[1 3 2]);

Because you are assigning the pixel i,j of each file using data from one file only, and also that should generate and error, I'm not quite sure how this code can run, as the size of Eight_Day_CMG_Snow_Cover is Kx1 and you are attempting to access elements i,j.

Anyway, that way is too cumbersome, avoid reshaping and using cell arrays by importing like this:

hdfvars =  {'Eight_Day_CMG_Snow_Cover', 'Eight_Day_CMG_Clear_Index',...
'Eight_Day_CMG_Cloud_Obscured', 'Snow_Spatial_QA'};
projectdir = 'E:\test\hdf test';
dinfo = dir( fullfile(projectdir, '*.hdf'));
num_files = length(dinfo);
filenames = fullfile(projectdir,{dinfo.name});

Eight_Day_CMG_Snow_Cover = zeros(3600,7200,num_files);
Eight_Day_CMG_Clear_Index = zeros(3600,7200,num_files);
Eight_Day_CMG_Cloud_Obscured = zeros(3600,7200,num_files);
Snow_Spatial_QA = zeros(3600,7200,num_files);

for K = 1 : num_files
    this_file = filenames{K};
    Eight_Day_CMG_Snow_Cover(:,:,K) = hdfread(this_file, hdfvars{1});
    Eight_Day_CMG_Clear_Index(:,:,K) = hdfread(this_file, hdfvars{2});
    Eight_Day_CMG_Cloud_Obscured(:,:,K) = hdfread(this_file, hdfvars{3});
    Snow_Spatial_QA(:,:,K) = hdfread(this_file, hdfvars{4});
end

So in this case, the variable Eight_Day_CMG_Snow_Cover contains what you wanted to put in B2.


Could a massive flood have formed the Grand Canyon?

tl;dr: no.

Long answer:

First of all, like mentioned by others in the comments, you would need some physical mechanism to take a whole lot of water, evaporate it, and drop it at once at a place where the Grand Canyon is now. This is not something that's going to happen because of physics. If there was some extraordinary event which could have caused this, we would get so much more evidence from it other than the Grand Canyon.

But let's assume, that somehow, by some magic or act-of-higher-being, an enormously large amount of water fell on the Colorado plateau at once. How would that look?

The huge amount rock removed from the plateau to form the canyon would have to go somewhere. And it would go there very violently, as huge boulders, irregular conglomerates and just a whole mess of mixed up geology. However, when you look downstream along the Colorado river, you don't see this stuff anywhere. When you look in the Gulf of California (which we know very well because of oil exploration), you don't see this stuff in there. Instead you see very nice sandstones, shales, marls, clays, etc. Definitely stuff that should not be there if there was a huge flood. Furthermore, those sedimentary rocks are interbedded with volcanic rocks and limestones. If this was a sudden event, all of this could not be there.
The Colorado river is meandering. If it was a huge sudden flood you would not get this morphology.
So far I was talking about lack of evidence. There is also some direct evidence for the incision rate of the Grand Canyon. Using a plethora of of scientific methods, such as radioactive isotope dating, cosmogenic dating, thermoluminescence, etc., it's possible to directly get a time when a certain thing happened (depending on the method). This allows us to know exactly when a certain rock formed, exposed, eroded (again, depending on the method). Using these scientific methods allowed us to infer that the Grand Canyon formed in the last several few million years, at an incision rate of a few dozen to a few hundred metres per one million year. The value depends on the exact location in the Grand Canyon and also on which research group you're asking. But whether the argument is about if it's 100 or 200 metres per million years is irrelevant for this discussion - it invalidates any suggestion that it formed in one sudden magical act of flood.


What causes dye to bleed from an agate stone?

Dyeing agate is a common practice- the stone is real, but it's been cosmetically altered. The stone gets soaked in a chemical solution, which is absorbed into the microscopic pores in the stone.  Whatever dye the person who made these coasters used must have been water-soluble.  So, yes, cold water would also cause the pigments to bleed.


Why pro-gradation occurs along coastal environments?

If deposition exceeds subsidence, then the water column shallows, the environment becomes more high energy, and so instead of muds and silt, the sediment would be coarser sand and gravel, so coarsening up.

If deposition and subsidence are equal, then the energy of the environment does not change, so similar grain sizes will be found throughout.

If subsidence exceeds deposition, then I'd expect the grain size to ultimately become smaller as the environment becomes more low-energy as the water column deepens. But what you wrote could refer to coarse material being less frequently reworked in such an environment.

Progradation merely refers here to the progression seen, as over time in the sediment size as a basin generally fills with sediments reflecting shallower environments, I think it's typically taught by the progression of sediments seen as a delta progrades into a basin. First with deep water muds, which coarsen upwards as the delta moves into the basin depositing sediment.

So what remains in the layered sediments we see, has a complex relation to the time of deposition, which might be more info than you require.(here's a link to a good diagram, which may help. LInk)


Similarities between grand circulation solvers and mantle convection solvers

Disclaimer: This is a partial answer given that my background is modeling of the ocean. I hope that some mantle convection modelers can complement this answer.

The question is good but the answer is complex. The short answer is:

No, they are not the same. Simply because computationally it would not make sense.

I will try my best to break it apart and make it as concise as possible.

Preface

As many people have pointed out scales are key. The environmental fluid dynamics problems we try to solve range enormous scales. However, every single motion is described by the Navier-Stokes (NS) equation, from the simplest flow you can think of all the way to the most complex - that includes turbulence (the continuum hypothesis says that the NS equations are valid when the Knudsen number $K_n \ll 1$).

Take a look at the chart below for oceanic processes alone. Temporal scales span 10 orders of magnitude, while spatial scales span 12 orders of magnitude. Presumably mantle solvers would extend the upper bounds on each of these scales.

The question you ask is specifically regarding oceanic grand circulation (OGC) models and mantle convection (MC) models. So according to the chart below, out of all oceanic models, the OGC and the MC models are the closest as far as temporal scales and spatial scales go.

The complexity of the Navier-Stokes equations and the difficulty in solving them

The Navier-Stokes system can be classified as a hybrid elliptic-hyperbolic type for steady flows and a hybrid parabolic-hyperbolic type for unsteady flows (the hyperbolic character comes from the continuity equation).

Where the nature of the equations say the following about each one and their respective numerical difficulties:

Hyperbolic nature is associated with wave phenomena and advective transport:

Fast waves lead to numerical stability restrictions
Nonlinear part of NS is hyperbolic ($\mathbf{u} \cdot \nabla \mathbf{u}$), which is the part of the equation that leads to turbulence.
Possibly one of the most difficult aspect of CFD is the propagation of sharp density fronts which are hyperbolic.

Parabolic nature is associated with diffusion and mass transport:

Boundary layers are governed by parabolic phenomena and are very thin compared to the environment that drives it. Notice the large scale disparity and associated numerical difficulty.
Turbulence, can be modeled from a parabolic perspective and this typically results in stability concerns on the numerical method employed.

Elliptic nature implies instantaneous propagation of information:

For environmental fluid dynamics, the nonhydrostatic pressure is of elliptic nature.
Although, theoretically, any disturbance propagates at infinite speed throughout the domain, numerical iteration sets a finite speed at which information can propagate.
Non-hydrostatic solvers have to invert a Poisson equation which is very computationally
expen  sive. In general, for the nonhydrostatic pressure, the 2-d problem requires the solution of a pentadiagonal, while the 3-d problem requires the solution of a
septadiagonal (7 diagonals) (not all near the main diagonal!).

Scales and numerical solvers

So as one can see by now, solving the NS equations numerically is not a trivial matter. Numerical solvers must face concerns regarding accuracy, stability and consistency, which pose constraints on the timestep and grid resolution that one can employ. See this answer regarding different approaches to numerical solvers. Scales are important for numerical solvers because of the nature of the system of the NS equations (described above) and the analytical mathematical techniques available to us to transcribe those equations to computational mathematical language. As it stands, it is impossible to resolve all temporal and spatial scales, so modelers resort to specific techniques (solvers) that apply to the problem (the scales) that they are interested in.

Conclusion

From their website:

  The MITgcm (MIT General Circulation Model) is a numerical model
  designed for study of the atmosphere, ocean, and climate. Its
  non-hydrostatic formulation enables it to simulate fluid phenomena
  over a wide range of scales; its adjoint capability enables it to be
  applied to parameter and state estimation problems. By employing fluid
  isomorphisms, one hydrodynamical kernel can be used to simulate flow
  in both the atmosphere and ocean.

and

  CitcomS is a finite element code designed to solve compressible
  thermochemical convection problems relevant to Earth's mantle.

My guess is they both use different numerical techniques to solve different versions of the Navier-Stokes equations that make the most sense given the scales of the problem that each one aims to resolve.


Catalog of all significant earthquakes since 1900?

Okay so I got the data from
https://earthquake.usgs.gov

Since the website only allows to get 20k records at a time I made a little python script that downloads data for each month.

import os
import requests

for x in range(1900,2016):
    for y in range(1,13):
        url='https://earthquake.usgs.gov/fdsnws/event/1/query.csv?starttime='+str(x)+'-'+str(y)+'-31%2023:59:59&endtime='+str(x)+'-'+ str(y+1) +'-31%2023:59:59&minmagnitude=2.5&orderby=time'
        print(url)
        response = requests.get(url)
        with open(os.path.join("C:/Users/kristjag/OneDrive/delo/potresi", "potresi_"+str(x)+"_"+str(y)+".csv"), 'wb') as f:
            f.write(response.content)

Code above take some time to finish but when completed you get earthquake data for every month from 1900-2016


Is there any intuitive and easily understandable way to help students understand the triangular facet of outcropted stratigraphy?

This is a tale of two three dimensional coordinate systems. You may need to take a bit of a side track if your students are not already familiar with the concepts of three dimensional coordinate systems, three dimensional vectors, the three dimensional dot and cross products, and transformations between coordinate systems. The image below portrays two such coordinate systems, with axes in blue and red,

I'll denote one of those coordinate systems as the East, North, Up coordinate system (ENU for short), with unit vectors $\hat E$ (East), $\hat N$ (North), and $\hat U$ (Up), and the other as the dip, strike and outward normal coordinate system (dsn for short), with unit vectors $\hat d$ (dip), $\hat s$ (strike), and $\hat n$ ((outward) normal). In terms of the above diagram, imagine the trio of blue vectors corresponding to East, North, and Up while the trio of red vectors corresponding to dip, strike, and (outward) normal. As an instructor, you might well want to create a better diagram, with labels.
There are singularity issues with both of these sets of coordinate systems. East/North/Up doesn't make sense at the Earth's poles as all horizontal directions are south at the North Pole and north at the South Pole. The dip/strike/normal doesn't make sense wherever the surface is flat. I'll ignore those issues. (Bonus question for your students: Address those singularities.)
One way to define local ENU coordinate system for a point on the surface of the Earth is to use the Earth's reference ellipsoid. The "up" unit vector is normal to the ellipsoid, with "up" pointing outward. The east unit vector is orthogonal to the $\hat U$ unit vector and the Earth's rotation axis. Use the cross product and divide by the norm. The north unit vector is the cross product of the up and east unit vectors.
Regarding the dip/strike/normal coordinate system, one direction, the outward normal, can be determined from the coordinates of the three points in a triangle in a vector-based triangular network digital elevation model. The coordinates of the three points in a triangle ($\vec A$, $\vec B$, $\vec C$) are supposed to be arranged such that $(\vec B - \vec A)\times (\vec C - \vec B)$ points outward. This points in the direction of $\hat n$: $\hat n = \frac{(\vec B - \vec A)\times (\vec C - \vec B)}{||(\vec B - \vec A)\times (\vec C - \vec B)||}$.
Aside: This is not always the case; there are DEMs that do not follow this rule. Those are DEMs one should toss in the nearest digital trashcan.)There are also some DEMs that do follow this rule but occasionally have triangles whose outward normal has a downward rather than upward component. An example is an extremely detailed DEM that models not only the topside but also the underside of an overhang. Satellite-based DEMS typically don't do this as satellites cannot see the underside of an overhang. (Extra credit: Have your students address such cases!)
We're looking for $\hat s$ and $\hat d$, the unit vectors that point in the strike and dip direction. Both of these vectors lie in the plane of the triangle formed by $\vec A$, $\vec B$, and $\vec C$. This means that both $\hat s$ and $\hat d$ are normal to the outward normal $\hat o$. The strike vector is horizontal, which means that it is also normal to the unit upward normal. To find $\hat s$, compute the cross product of $\hat U$ and $\hat n$ and normalize. (Note: Normalization is impossible if $\hat U \times \hat n \equiv 0$. This only happens if $\hat n$ is parallel or antiparallel to $\hat U$, in which case strike and dip are not well-defined.) Finally, the dip unit vector, which is what we're after, is the cross product of the strike and outward normal unit vectors: $\hat d = \vec s \times \vec n$. Bonus: Ask your students to show that if $\hat d$ exists, it always has a downward component.


Is it less windy in a valley than on a hill?

Scenario #1 is relatively, somewhat, kinda, but-not-really accurate for summer, scenario #2 more so for winter. If you live in or near mountains, you are familiar with cold evening winds that come ripping through canyons and valleys to settle in basins. In mountainous vineyards of Europe, vine layout is routinely adjusted to dissipate these winds. Even if there is NO (weather-induced) wind, thermal gradients can create winds of their own.

Winters see less of these thermal gradients and in general winds will be limited to higher altitudes where they blow pretty consistently. In both of your scenarios, however, winds blow pretty consistently at higher elevations....which is why wind farms are typically found there.


How do meteorologists grade their past forecasts?

Disclaimer: I am not a meteorologist.

To answer this question, you need to understand how a forecast is obtained. Basically, meteorologists run computer simulations that predict how weather systems might evolve from current measurements. In fact, data assimilation techniques (http://en.wikipedia.org/wiki/Data_assimilation) are commonly used, so that the predictions given by the model depend not only on current observations, but also upon the history of the weather system. As new data comes in, the evolving model is adjusted towards those values - but not forced to match exactly, as that would destroy the historical information.

Any one model is therefore not expected to be entirely accurate. However, if we run many models simultaneously, and include random variations to reflect the chaotic nature of the weather system (and measurement errors), we obtain something called an "ensemble forecast". This is where the 30% chance of rain comes from: 30% of the simulations predict rain in your area, while the others think it will remain dry. Average/minimum/maximum temperatures etc can also easily be extracted from the ensemble.

So, the accuracy of a forecast can be considered in different ways. It is certainly possible to compare the predictions of one ensemble member to observations: this is an integral part of the data assimilation process. However, it is not necessarily a problem if a member performs poorly on one particular occasion. Different types of measurements may be weighted differently during the comparison (so matching rainfall might be deemed 'more important' than matching temperature; this may vary depending on the type of forecast/its intended use). More generally, you have to look at the long-term statistics of the errors in the forecast. So, if you look at several months of data, did it rain on around a third of the days when you predicted a 30% chance of rain? On average, are your temperature predictions reasonably accurate? Making this assessment on a day-by-day basis is unlikely to be in keeping with the statistical basis of modern weather forecasting.


Looking for a complete table of minerals for a database

After looking at the source from nrmima.nrm.se/IMA_Master_List_2016-03.pdf, I discovered the:
IMA Database of Mineral Properties
Created and maintained by the RRUFF Project in partnership with the IMA
http://rruff.info/ima/

I downloaded all the minerals in a csv file.  This worked very well for two reasons.  First they had various chemical formula formats that were favorable for entry into a database.  Second they had a mineral group column.  The mineral group column was very important since often in the field the exact mineral is not noted but a group such as chlorite or garnet.  I will worry about Strunz or Dana numbers/codes at a later date.


Adding time dimension and corresponding new variable to a netcdf file

There are two issues (and one optimization) with/for the code above:

Array indexing starts with 0 (not 1) in Python. Therefore, it should be if data[0,m] == countries[i,j]: because you are interested in the first row;
m goes from 0 to 1 (for m in xrange(0,1):) but it should go from 0 to number_of_countries so that all columns of the energy are iterated.
The order of the n and m for loops seems to be quite ineffective. Better would be: for m ...: if data[1,m] == ...: for n in ...: nc.variables...=.... Or replace latter for loop by nc.variables["energy"][:,i,j]=data[:,m]

This could work (please improve => community Wiki) but I cannot test it due to missing input data:

y = nc.createDimension("y", 950)  #y
x = nc.createDimension("x", 1000) #x
time = nc.createDimension("time", 87648) #time

latitude=nc.createVariable("y", "f8", ("y", ))
longitude=nc.createVariable("x", "f8", ("x", ))
time = nc.createVariable("time","f8",("time",))
countries = nc.createVariable("countries", "f8", ("y", "x"));
energy = nc.createVariable("energy", "f8", ("time", "y", "x"),
                           fill_value=-9999, chunksizes=(1, 950, 1000));

time.standard_name='time'
time.units ='hours since 2006-01-01 00:00:00.0'
time.calendar='proleptic_gregorian'

lats =  np.arange(5497500,747500,-5000)    #y
lons =  np.arange(2502500,7502500,5000)    #x

# Fill variables
latitude[:] = lats
longitude[:] = lons

countries [:]=value

for i in xrange(0,950):  # grid rows
    for j in xrange(0,1000):    # grid columns
        for m in xrange(0,int(max(countries))):    # countries
            if data[0,m] == countries[i,j]:   #correspondance
                nc.variables["energy"][:,i,j]=data[1:87648,m]


looks like a broken block of glass from a glass factory, called glass slag or chunk glass, you can get a whole range of hardness and color depending on what type of glass they were making. People often use it to make art.


Is there a seasonal difference in the average cloud floor?

Cloud base varies as response to several factors, normally, cloud bases are lower in the wintertime than during the summer, particularly for stratus clouds.

Cloud development is mostly dependent on temperature and the amount of water vapor in the air. The temperature has to be low enough to cause the air to become saturated. If the air is relatively dry, a colder temperature is required for water vapor to condense into water droplets. During winter, the temperature of the air in the lower part of the atmosphere is lower than during the summer, so stratus clouds can usually form at somewhat lower heights during the winter than during the summer.

This is a generalization that does not hold true under all conditions and at all times.

Also some more regional patterns can be present since the differentiation between Summer and Winter can be difficult to establish, for example in the tropics.

Taken from: https://stab-iitb.org/newton-mirror/askasci/wea00/wea00274.htm


download NASA Earth Exchange Global Daily Downscaled Projections for single lat lon

I did download and process some of the NEX files a year or so ago. Maybe there is a more efficient way to do that now, but what I used is this R script to download the files:

library(RCurl)
opts = curlOptions(proxy='http://10.xxx.xxx.xx:xxxx', userpwd = "you needed NEXGDDPusr:pswd at that time", netrc = TRUE)

var = c("pr","tasmax","tasmin") ##here the variables
rcp = c("rcp45", "rcp85") ##here the scenarios
mod= c("CCSM4", "GFDL-CM3", "IPSL-CM5A-MR", "MIROC5",  "inmcm4")##here the climate models
year=c(seq(2040,2060,1)) # here the time period

for (v in var) {
  for (r in rcp) {
    url<- paste0( 'http://nasanex.s3.amazonaws.com/NEX-GDDP/BCSD/', r, '/day/atmos/', v, '/r1i1p1/v1.0/', sep='') ##here the url (please, note this might be different now)
    for (m in mod) {
      for (y in year) {
        nfile<- paste0(v,'_day_BCSD_',r,"_r1i1p1_",m,'_',y,'.nc', sep='')
        url1<- paste0(url,nfile, sep='')
        destfile<-paste0('D:/NASA_NEX/',r,'/',v,'/',nfile, sep='') ##destination folder
        bin <- getBinaryURL(url1, .opts = opts)
        writeBin(bin, destfile)
        Sys.sleep(1)
        gc()
      }}}}

This, however downloads the global files.

The second stage would be to extract the data for the point you need. For doing this in R you can use something like this. In alternative you can use CDO.


Photosynthesis increase in the northern hemisphere spring, especially in the Siberian Taiga, and carbon dioxide decrease in atmosphere. During autumn and winter, decomposition of plant material and increased combustion, bring more carbon dioxide to the atmosphere. Measurements from e.g. the South Pole shows the same pattern, but much less and in opposite phase.  As the temperate and boreal landmass are larger on the northern hemisphere, the oscillation is also larger.

It's well explained here. Also check ESRL and CDIAC for more stations and downloadable data sets.


Algorithmically Determining the Rainy Season

This is an interesting statistics problem.  I would do this for starters.  Take all the years of data and calculate average rainfall amounts for each day of the year.  If you're looking for the rainy season, I would use maybe a 30-day moving average on this, but make sure that the average can wrap around Jan 1.  Once you have these values -- 365 values for each site -- I suggest trying a few things:

1) Try out the standard deviation approach.  It's easy to calculate, so you should be able to play around with the particular number "x" in "x standard deviations above the mean".  If it works for you, there's no reason to make it harder.

2) Using "x standard deviations above the mean" really just means that you're looking for a certain value of the cumulative distribution function (CDF) of rainfall.  Instead of using standard deviation, you could calculate these CDFs for each site from those 365 values, and pick the 75th percentile or something.  I'm still thinking through whether this would guarantee that you get a "season"; it's possible that the top-percentile values of the year are nowhere near each other.

3) Plot out a few distributions for key sites: dry midlatitudes, wet midlatitudes, polar, tropics (monsoon-dominated).  If these PDFs look pretty similar, you might be able to figure out a sensible approach.

I think the key difficulty here isn't going to be that different places have different total rainfall, but that different places can have rainy seasons of different lengths.  If the season was always 30 days, I think this approach would be fine.  I'm really interested in seeing those CDFs just to see how different they are.

Let us know what you come up with!


Looking for evidence against some points raised in Ivar Giaever's speech on climate change

Ivar's graph is mischievous in trying to imply that there should be an exact correlation. The global air temperature is not a good parameter to chart against CO2 emissions because the increasing heat doesn't just warm up the air. Most of the heat goes into warming the upper layers of the oceans, but it is not a linear relationship. The ratio of air and ocean warming varies massively with the state of ocean current, in which El Nino is just the largest of several ocean oscillations. If, instead of graphing the global air temperature, he graphed the global heat absorbtion, he would have arrived at a much more uniform relationship to greenhouse gas emissions.
Ivar is an engineer and a physicist, not a climate scientist, and his inability (or unwillingness) to look at the holistic picture makes me doubt his climate change credentials.


Is there a buildup of static electricity in a sandstorm?

Sandstorms occur in dry areas, particularly deserts. The sand particles are fairly uniform in size. As the dry sand moves due to the wind it begins a leaping process known as saltation. Quoting the Wikipedia article:

  the initial saltation of sand particles induces a static electric field by friction

The sand particles become polarized and as the particles begin to hit each other the polarization on each grain gets rearranged and the degree of polarization doubles with each interaction. With a build up of static electric charge lightning can develop.

The greatest build up of static charge occurs in sandstorms of intermediate thickness.


Karkar is made of calcium carbonate.

then why it happens only in arid soil?

Because this is what happens to calcium carbonate in wet environments:

It dissolves away, forming a cave. Calcium carbonate, or calcite, is soluble in mildly acidic solutions. Rainwater is mildly acidic because of CO2 from the atmosphere dissolving in it, so it never allows for the formation of calcium carbonate deposits in the soil to begin with.
As for why it forms in arid soils, the Wikipedia page for Caliche explains it well:

Caliche generally forms when minerals leach from the upper layer of the soil (the A horizon) and accumulate in the next layer (the B horizon), at depths around 3 to 10 feet under the surface. It generally consists of carbonates in semiarid regions—in arid regions, less-soluble minerals form caliche layers after all the carbonates have been leached from the soil. The deposited calcium carbonate accumulates—first forming grains, then small clumps, then a discernible layer, and finally, a thicker, solid bed. As the caliche layer forms, the layer gradually becomes deeper, and eventually moves into the parent material, which lies under the upper soil horizons.
However, caliche also forms in other ways. It can form when water rises through capillary action. In an arid region, rainwater sinks into the ground very quickly. Later, as the surface dries out, the water below the surface rises, carrying up dissolved minerals from lower layers. This water movement forms a caliche that tends to grow thinner and branch out as it nears the surface. Plants can contribute to the formation of caliche, as well. Plant roots take up water through transpiration, and leave behind the dissolved calcium carbonate, which precipitates to form caliche. It can also form on outcrops of porous rocks or in rock fissures where water is trapped and evaporates. In general, caliche deposition is a slow process, but if enough moisture is present in an otherwise arid site, it can accumulate fast enough to block a drain pipe.

(photo from http://www.naturephoto-cz.com/karst-cave-photo-24442.html)


What does the term "topology" mean in geology?

Topology is a study of deformable shapes and connectivity. Topography is a study of more or less non-deformable shapes. A coffee cup that has an intact handle and a donut with a hole in the middle are equivalent shapes topologically, but obviously are not equivalent shapes topographically.


Optimal vertical coordinate for complex topography

This is a big question, but there are benefits and difficulties with all representations, and the efficacy of any coordinate will also depend upon other numerical choices such as the calculation of horizontal pressure gradients [Zängl 2012].  There are broadly two categories: terrain following layers and Cartesian systems such as step terrain and cut cell grids.

Terrain following layers are typically implemented using a coordinate transformation such that the computational domain is rectangular.  There are essentially two choices to make here.  First, the vertical coordinate may be geometric height, pressure, or entropy (potential temperature), or some other monotonic function.  Second, the decay function controls how the influence of the terrain on computational surfaces decays with height: this might be a linear decay (called Basic Terrain Following (BTF) or 'sigma' coordinates [Gal-Chen & Somerville 1975]), or something more elaborate such as Smooth LEvel VErtical (SLEVE) [Schär et al 2002] or Smoothed Terrain Following (STF) [Klemp 2011].

One of the motivations for smoothing coordinate surfaces is to make horizontal pressure gradient calculations more accurate: in areas of steep terrain metric terms in the coordinate transformation can increase errors [Dempsey & Davis 1998].  This is also one of the motivations for step terrain and cut cell grids, since the grid is orthogonal everywhere except in the cells next to the ground.

Another issue with representing terrain is that cells can become very thin (over steep slopes with terrain following layers) or very small (in a cut cell or partial-step grid) which constrains the timestep for explicit numeric methods due to the CFL criteria [Klein 2009].  There are several methods that can overcome this 'small cell problem' [Jebens et al 2011, Yamazaki & Satomura 2010, Steppeler et al. 2002]

This is by no means an exhaustive answer but I hope it gives you some understanding of the issues involved with vertical coordinates.  Additionally, I gave a presentation in 2015 on this topic, the slides are available online.  Another useful review article on nonhydrostatic models, including terrain representation and vertical coordinates, is presented in Steppeler et al 2003.

References

Dempsey, D. and C. Davis, 1998: Error analyses and test of pressure gradient force schemes in nonhydrostatic, mesoscale model. 12th Conf. on Numerical Weather Prediction, Phoenix, AZ. Amer. Meteor. Soc., 236–239
Gal-Chen, T. and R. C. Somerville, 1975: On the Use of a Coordinate Transformation for the Solution of the Navier-Stokes Equations. J. Comp. Phys., 17, 209–228
Jebens, S., O. Knoth and R. Weiner, 2011: Partially implicit peer methods for the compressible Euler equations. J. Comp. Phys., 230, 4955–4974.
Schär, C., D. Leuenberger, O. Fuhrer, D. Lüthi and C. Girard, 2002: A new terrain-following vertical coordinate formulation for atmospheric prediction models. Mon. Wea. Rev., 130, 2459–2480
Steppeler, J., H.-W. Bitzer, M. Minotte and L. Bonaventura, 2002: Nonhydrostatic Atmospheric Modeling using a z-Coordinate Representation. Mon. Wea. Rev., 130, 2143–2149
Steppeler, J., R. Hess, U. Schättler and L. Bonaventura, 2003: Review of numerical methods for nonhydrostatic weather prediction models. Meteorology and Atmospheric Physics, 82, 287–301
Yamazaki, H. and T. Satomura, 2010: Nonhydrostatic Atmospheric Modeling Using a Combined Cartesian Grid. Mon. Wea. Rev., 138, 3932–3945
Zängl, G., 2012: Extending the numerical stability limit of terrain-following coordinate models over steep slopes. Mon. Wea. Rev., 140, 3722–3733


In seismic data analysis we have to distinguish between vertical (temporal) and horizontal resolution.

The (First) Fresnelzone is linked to the vertical resolution and defines the area in which  the collected energy will still stack constructively.

The temporal resolution however, defines what the wave "sees". The events can still be resolved if their thickness is within $\frac{\lambda}{4}$. However, in some cases it can go down to $\frac{\lambda}{32}$. This is a matter of detectability of events.

Don't forget that we're dealing with superpositions of different frequencies so basically low frequencies will give use the necesseray energy content of general trends, while high frequencies enable us to make a distinction between geologic features.

This file from the university of Oslo gives a nice overview.

(The picture is from a blog post I've written on knowing your wavelength it's CC-BY-SA Source1 Source2


What is the technical term for the amount of water within the surrounding enviornment?

What is capped at 100% is the relative humidity, that describes the degree of water saturation of air. Relative humidity only consider the water that is dissolved in the air, not all the water that exists in a given volume of air.

You are interested in the mixing ratio between air and water, which is referred to as specific humidity. Specific humidity is the ratio of the mass of water to the total mass of the moist air parcel.

That water, usually refers only to the one dissolved in the air parcel. However, it can easily be extended to include water droplets and other bodies of water. But that approach will require that you define an arbitrary volume over to which compute the mixing ratio, because you won't be able to treat this as a continuum problem.

For instance you can compute the mean mixing ratio between air and water on a 10 meters sphere around your object of interest. Then, the more water there is within that volume, the higher will be your mixing ratio. For example, if a person is swimming 10 m deep, the mixing ratio would be almost 1. And if the same person is in the desert more than 10 m away from any water body, the mixing ration will be close to zero (not cero because all the water in the person's body and what is always in the air).


Do any rivers deposit sediment in their deltas fast enough to counteract sea level rise?

This is a hugely complex subject for which a categorical answer requires detailed river basin modelling and process calibration. The short answer is that in the short term many rivers deposit sediment much more rapidly than the hypothetical 1m per century, because sediment transport is roughly proportional to the fourth power of the stream velocity. So enormously high deposition occurs in the few hours per year of peak discharge. Mean annual deposition, however, is a different issue, and inordinately difficult to measure. A typical mean rate of sedimentary deposition is roughly 2 to 3 mm per year +/- at least an order of magnitude. This is slightly less than the current mean rate of sea-level rise, but remembering that the rate of sea-level rise is accelerating, and is likely to continue accelerating well into the next century. Projected rates of sea level rise by 2100 are tricky to estimate (too many unknowns) but >10 mm/year seems likely, in which case probably no significant rivers will be able to compensate by high sediment deposition. There will be occasional anomalies, such as the rivers around Mount Pinatubo in the Philippines, which had jaw-dropping sediment loads for the few years after the volcano erupted, caused by huge quantities of un-cemented volcanic ash over entire catchments, coupled with typhoon rainfall.

In the case of really big rivers with perennially high sediment loads, one thinks of the Mekong and the Ganges-Meghna-Brahmaputra, both of which have huge monsoonal sediment fluxes. Both river systems have pro-graded their deltas surprisingly recently. Check out their delta areas on Google Earth. All of that sediment was deposited during the last 6000 years. During most of that time, sea level rise seems to have been very modest, averaging about 1.5 to 2.5 mm/year.
Since you pose a climate-change related rate of sea level rise, one must also consider how climate change will affect the hydrologic response of these catchments, and hence how the rate of sedimentation will change? Even with a three-year research grant I would be hard-pressed to answer that question, but my suspicion is that sea-level rise will win, and most of the major deltas will succumb to the waves.


Why don't iron meteorites on Mars rust or oxidize? Why are they shiny?

tl;dr: Compared to Earth, the atmosphere on Mars is very thin; in addition, it contains much less of oxygen and water (i.e., is very dry). It is much colder there. These conditions may slow down oxidation to an irrelevant rate.

For an object of pure iron, note that according to the English Wikipedia about Mars:

  "The atmosphere of Mars consists of about 96% carbon dioxide, 1.93%
  argon and 1.89% nitrogen along with traces of oxygen and water."

This contributes as plausible contribution for a lesser rate of oxidation than on earth because the concentration of an oxidizer (i.e., oxygen) is much lower (0.146 vol%, according to the side table, ibid.)  if one assumes absence of processes splitting the $\ce{CO2}$ to yield oxygen.  To set these values into context (entry Earth):

  "A dry atmosphere is composed of 78.084% nitrogen, 20.946% oxygen,
  0.934% argon, and trace amounts of carbon dioxide and other gaseous molecules. [...] Water vapor content varies between 0.01% and 4% but averages about 1%."

Equally note that the reported surface temperatures on Mars (min 130 K, mean 210 K, max 308 K) are substantially lower, than on Earth (min 184 K, mean 287.16 K, max 330 K) -- equally slowing oxidation.

About water and atmosphere humidity:  Provided the meteorites get water wetted, said perchlorates could start to work as oxidizer of iron.  In comparison to this, dry perchlorate will remain inactive.  Again, Mars' atmosphere (0.0210 vol% of water vapour, ibid.) seems to be considerably more conservative than Earth's.

You can't have oxidation alone, it is a electrochemical process where some other material in contact with the one oxidized has to be reduced. Corrosion of the meteorites equally could occur if these simultaneously contain grains of metals / alloys differing in the their electrode potential provided

these grains  were in close contact with each other, especially if their grain boundary were water wetted, and
if one of the metals / alloys -- relatively speaking -- would be «less noble», i.e. a material with a more negative reduction potential, than any other grain / material in contact.  The less noble grain than would become a galvanic anode and eventually be "eaten away", protecting the other, more noble one, to be oxidized.(table)  The formation of these little, local electrochemical cells could be even more important if there were salty water droplets on the surface at room temperature and above accelerating corrosion.  (This, by the way, a reason why steel bridges and copper house roofs exposed to the climate and atmosphere of salty seas are more prone to galvanic corrosion than similar constructions only exposed to fresh water.)  Presence of phosphates in said meteorites however could slow down the oxidation of the meteorites (passivation, as assumed for the Iron pillar in Dehli).

Note: The initial answer assumed iron meteorites to contain discrete grains, occasionally altogether with grains of other metals.  A comment by@KenFabian pointed out iron meteorites however mainly consist of (Fe,Ni) alloys.  Their electrochemical properties may be different to their components (e.g, table), which the edit aims to include.


Where can I download historical sesimic data in CSV/Excel format for processing in R?

You can download seismic data in csv format at USGS, from past hour, past day, past week and past month.

If you wish to download ancient data you need to use the USGS API. There is a query option: format=csv.

This is an example of an URL that gives you seismic data in csv format from 2014-01-01 to 2014-01-02:

https://earthquake.usgs.gov/fdsnws/event/1/query?format=csv&starttime=2014-01-01&endtime=2014-01-02

User Kristjan shared a Python script to ask the API for all available data since 1900.


Did Jupiter really make Earth (in)habitable?

Perhaps, but it's tough to tell.

I wrote up an answer for a question on Physics getting at some more specific issues here; bear with me if I re-use a little bit of it.

  How is it physically possible for such a migration to occur?

Planetary migration in the Solar System is described by the Nice Model (or, actually, the Nice II Model, its successor). Basically, the gas giants started out at different orbital semi-major axes than they are in today. There is one change in order, though: Neptune is closer in than Uranus (making the order Jupiter, Saturn, Neptune, Uranus). Also, there may have been a 5th giant (actually an ice giant), which was subsequently ejected. It would have been similar to Uranus and Neptune in composition and size.

Here's a quick summary of the Nice Model:

Planetesimals and Kuiper Belt Objects interact with the giants, moving Jupiter inwards a tad and the other three (or four) outwards.
Eventually, resonances come into play. These are ratios between orbital periods. Jupiter and Saturn cross a mutual threshold.
Saturn moves outward and Jupiter moves inward. A chain of events happens, where Neptune and Uranus are dramatically moved outwards, with Neptune being chucked to the rear (and the hypothetical 5th giant being ejected). You can watch the video given in that question here.

Here are the before and after pictures, stolen borrowed from that question:

I can, specifically, recommend the papers I mentioned there: Fassett & Minton (2013), Deienno & Nesvorny (2014), Brasser et al. (2009), Nesvorny (2011), Nesvorny & Morbidelli (2012), and Gomes, Levison, Tsiganis and Morbidelli (2005) - in no particular order, I think.

  Is there a way to verify the migration hypothesis with observations?

Well, simulations were used in some of the mentioned papers, especially in those co-authored by Nesvorny, who was involved in recent developments of the Nice Model. These lead to the hypothetical 5th giant, because in some cases, it is necessary as a sort of sacrificial lamb, to prevent Uranus or Neptune from being flung out.

We really can't observe this happening in real time, so simulations are our best shot.

  How different would have been the hypothetical planets in the area currently occupied by Earth?

The Late Heavy Bombardment might not have happened. That's a whole avenue of investigation into alternative astronomical history. It would involve fewer craters and possibly less fewer small collisions between some objects. It would be a calmer place. No migration = no LHB.

Beyond that, it's hard for anyone to say. Detailed simulations are really difficult, because the exact conditions that matter - not just orbital positions - are hard to model.

  If such a migration toward the Sun started, what prevented Jupiter from continuing inward and collapsing with the Sun?

There just wasn't enough impetus from the encounter with Saturn to fling it inwards. A migration isn't just a planet rushing willy-nilly into space; it's a delicate system of slight orbital changes. All odds were against something like this happening.

  Is it possible for such a migration to occur again?

Only if the right resonances appear, which is unlikely.