Equations for Milankovitch Factors? Are there equations for the radiative forcings of the three Milankovitch factors at any given time? Or a database of values?

There are many models that estimate orbital parameters at long timescales. All of them rely in rather complicated formulas that account for the perturbations generated by other planets, the Moon, and many other factors. Some of the models even consider relativistic effects as described in this answer.

An orbital solution that I often see cited and used in papers dealing with climatic forcing is LA93, computed by Laskar et al. (1993) and described in the paper Orbital, precessional, and insolation quantities for the Earth from -20 MYR to +10 MYR. However, more accurate solutions are available by now, like LA2004 and more notably LA2010. I'll describe below how to download output data and codes for each of these solutions.

For LA93, the codes (in fortran) and outputs of that model are available at the VizieR catalogue, where you can query the model output, or just go into this FTP folder and download the files for the code and long tables with the model outputs every one thousand years.

If by "the three Milankovitch factors" you mean Obliquity, Eccentricity, and   Precession index (Eccentricity $\times$ sin(longitude of perihelion)). That data is ready to download and use in these two files:

CLIVAR0N.ASC (-20Myr to 0Myr)
CLIVAR0P.ASC (0Myr to +10Myr)

The byte-per-byte description of these files as described in the README is

--------------------------------------------------------------------------------
   Bytes Format  Units   Label     Explanations
--------------------------------------------------------------------------------
   4- 11  F8.0   1000yr  t         Time from J2000 in 1000 years
  13- 31  D19.12 ---     e         Eccentricity
  33- 51  D19.12 rad     eps       obliquity (radians)
  53- 71  D19.12 ---     CP        e * sin(longitude of perihelion
                                           from moving equinox)
--------------------------------------------------------------------------------

With the code you can also compute common climatic proxies such as isolation at 65°N or any other latitude.

For LA2004 (-50 Myr to +20 Myr) the same research team implemented a very useful online calculator that allows to compute insolation quantities derived from the orbital and precessional quantities for any latitude of interest. It outputs the results in tabular form. The calculator interface looks like this so you get an idea of the available options.

The LA2010 solution is described in the paper La2010: a new orbital solution for the long-term motion of the Earth, and goes 250 Myr back in time. You can find similar data files and codes for this solution at the Astronomical Solutions for Earth Paleoclimates page. To my understanding, LA2010 represent the state-of-the-art solution for orbital parameters and it does include relativistic corrections as well.


Why is WRF-Chem not reading my emissions data? I've been trying to run WRF-Chem with my emissions but they are not being read by WRF-Chem. RADM2.

I have emissions for the whole week, so io_style_emissions = 2 and the emissions are in wrfchem_d01_2014_08_04_00:00:00 format, just one emissions file. I thought io_form_auxinput5 == 11, but I have error I had to comment that line in namelist.input, and maybe that is the reason. I could model with 2 12-hours emissions files but that is not my goal, because I developed the emissions model, with high resolution temporal and spatial, and I would lose to much information otherwise.

The whole configuration for running WRF is here.

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

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

 &domains
 time_step                           = 6,
 time_step_fract_num                 = 0,
 time_step_fract_den                 = 1,
 max_dom                             = 1,
 e_we                                = 191,
 e_sn                                = 181,
 e_vert                              = 35,
 p_top_requested                     = 5000,
 num_metgrid_levels                  = 27,
 vert_refine_fact                    = 1,
 num_metgrid_soil_levels             = 4,
 dx                                  = 1000,
 dy                                  = 1000,
 grid_id                             = 3,
 parent_id                           = 2,
 i_parent_start                      = 57,
 j_parent_start                      = 59,
 parent_grid_ratio                   = 5,
 parent_time_step_ratio              = 5,
 feedback                            = 1,
 smooth_option                       = 0
 /

 &physics
 mp_physics                          = 2,
 ra_lw_physics                       = 1,
 ra_sw_physics                       = 2,
 radt                                = 15,
 sf_sfclay_physics                   = 1,
 sf_surface_physics                  = 2,
 bl_pbl_physics                      = 1,
 bldt                                = 0,
 cu_physics                          = 0,
 cudt                                = 0,
 cu_diag                             = 1,
 cu_rad_feedback                     = .true.,
 isfflx                              = 1,
 ifsnow                              = 0,
 icloud                              = 0,
 surface_input_source                = 1,
 num_soil_layers                     = 4,
 num_land_cat                        = 20,
 sf_urban_physics                    = 1,
 /

 &fdda
 /

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

 &bdy_control
 spec_bdy_width                      = 5,
 spec_zone                           = 1,
 relax_zone                          = 4,
 specified                           = .true., .false.,.false.,
 nested                              = .false., .true., .true.,
 /

 &grib2
 /

 &chem
 kemit                               = 1,
 chem_opt                            = 1,
 bioemdt                             = 15,
 photdt                              = 15,
 chemdt                              = 2.,
 io_style_emissions                  = 2,
 emiss_inpt_opt                      = 1,
 emiss_opt                           = 2,
 chem_in_opt                         = 1,
 phot_opt                            = 2,
 gas_drydep_opt                      = 1,
 aer_drydep_opt                      = 0,
 bio_emiss_opt                       = 0,
 dust_opt                            = 0,
 dmsemis_opt                         = 0,
 seas_opt                            = 0,
 gas_bc_opt                          = 1,
 gas_ic_opt                          = 1,
 aer_bc_opt                          = 1,
 aer_ic_opt                          = 1,
 aer_op_opt                          = 0,
! opt_pars_out                       = 0,
 gaschem_onoff                       = 1,
 aerchem_onoff                       = 1,
 wetscav_onoff                       = 0,
 cldchem_onoff                       = 0,
 vertmix_onoff                       = 1,
 chem_conv_tr                        = 0,
 biomass_burn_opt                    = 0,
 aer_ra_feedback                     = 0
 have_bcs_chem                       = .false., .false., .false.,
 /

 &namelist_quilt
 nio_tasks_per_group = 0,
 nio_groups = 1,
/


Why does glacier ice look blue? The color of the ice observed in glaciers, icebergs and crevasses is often blue. However, ice cubes and industrial ice blocks are perfectly transparent or white if not.
So. Why does glacier ice look blue?
Some examples of what I mean:
Ice blocks

Glacier ice

PS: As a glaciologist, this is a question I've received a lot, and after Googling for it, the answers I found are not fully satisfactory, so I decided to share mine here in a Q&A format.

The short answer is: BECAUSE THE ICE IS BLUE.
Now we have to explain why it seems perfectly transparent on ice cubes and industrial ice blocks.

It has to do with the fact that most transparent materials are not perfectly transparent, and instead absorb (and/or scatter) part of the light that hits them. And when the transparency is better for one specific color (wavelength), the material will look that color, much like how stained glass gives a tint to the white light going through it.

Now the important fact is that this "tint" effect is stronger the thicker the material is. Let's take a standard glass as a example: When looking through the glass it seems perfectly transparent, because the thickness is too small to make its color noticeable. However, if you look at the edge of the glass, it looks green, which shows that glass is indeed green, but that only became noticeable when looking through a section thick enough.

(Picture taken from peakglass.ca)

In the same way, glacier ice is blue, but that can only be appreciated if the piece of ice you are looking at is thick enough. We can see this effect in the following picture of an iceberg, where sections of different thicknesses displays a color gradient ranging from transparent to blue.

The remaining questions is: Why are glaciers not always blue? As we know, many icebergs (and snow) are white.

The key here is "bubbles". The reason is that for each ice/air interface, part of the light gets reflected and only a fraction goes through. Therefore the greater the number of bubbles a piece of ice has, the less light will be able to proceed deeper into the ice.  As such, when you view light from more bubbly ice, most of it will have traveled through less ice, and thus have had less distance to obtain the blue tint. I've made this figure to help understanding the idea:

On the left, a piece of ice with very few bubbles allows a longer travel path through the ice, inducing a marked blue tint in the outgoing light. On the right, in contrast, if there are many bubbles the light will go bounce back right away with only a very subtle tint, or no tint at all.

Snow is the extreme of this, because it is mostly air (bubbles), and only some small pieces of ice, that's why it looks so white.

An interesting ramification of this: those deep blue pieces of ice started as snow. But as the snow piles up, the weight of the snow on top compacts it, squeezing air out changing it from snow to firn until you finally reach the density of being a block of ice (with bubbles). In a big glacier, hundreds or thousands of meters of ice can exert such a pressure that the bubble spaces get compacted smaller and smaller until they disappear and the air is dissolved into the ice itself.
Therefore this means that the more blue (and bubble-less) a piece of ice is, the more it has come from deeper within the glacier... meaning it also has traveled from further up in the glacier basin and it is, therefore, older. So, now you can identify deep, old ice just by its color.

To give a more familiar example of how adding bubbles can turn something transparent with a tint into something white, take the case of egg whites, which are transparent with a yellowish tint. But as you whip it, adding in bubbles, it begins to turn into meringue which is perfectly white.

However, there is a trick that can even make the blue color of seemingly-white snow noticeable. If you let a ray of light bounce on it many times, each time it will travel through a little bit of ice, getting a bit of blue tint each time, and after enough bounces it will look unquestionably blue. That's what you see when looking at the entrance of a snow cave or a crevasse in a glacier. The light that comes from deeper in the cave has bounced many more times around the walls and becomes bluer:

(Picture taken from outdoorlifenorway.com)


The origin of spurious high frequency waves in numerical weather prediction I am currently reading this paper on digital filters and their relevance to meteorological models Digital Filter Initialization . My question relates to this following text - "The requirement to modify meteorological analysis to avoid spurious high frequency oscillations has been known from the beginning of numerical weather prediction". Is it possible to get a mathematical illustration of how this problem originally arises(i.e. spurious high frequency oscillations) in numerical weather prediction ?
I am not sure whether I can edit this question but I would like to restrict the domain of the question to high frequency oscillations arising in  high resolution simulations and one that involves a LES. Numerically I have four nested grids - my country, a state within that and a district within that state and then a small valley inside that district. My question relates to the high frequency oscillations inside the innermost grid. Let us assume model independence for this question.

I don't have a mathematical illustration at the moment (though I might edit this answer if I come across something in my notes). But the basic problem is sound waves. If you make a numerical model of the full Navier-Stokes equations, that model has to allow sound waves to propagate. That implies that the time step used has to be incredibly short -- under 5 minutes for sure for a grid size of 100 km, and preferably much shorter than that. So sound waves are fast-moving, high frequency, and don't really contribute to weather. That's why weather prediction models tend to use the primitive equations: these equations are very similar to the Navier-Stokes, but sound waves aren't solutions to these equations.

Hopefully that's a start. Feel free to ask for a clarification on how this filtering is done in practice. Like I said, I'll look through my notes for something, I remember doing a problem set involving this.

Edit: Here are some notes from David Randall on the anelastic and Boussinesq approximations. These notes are in more of a classroom format, but at the end they derive the link between the continuity equation and sound waves.

Briefly, sound waves arise from variations in density that are independent of variations in enthalpy/temperature. The continuity equation in general is $ \frac{\partial \rho}{\partial t} + \nabla\cdot (\rho \vec{u}) = 0 $ in which density variations can be accompanied by contraction and expansion of the fluid. The anelastic equations filter out sound waves by replacing this dynamic equation with a diagnostic one, removing the time derivative: $ \nabla\cdot(\rho_0 \vec{u}) = 0 $ where $\rho_0$ is a background density profile. No more time derivative for density means no more expansion and contraction except under changes in enthalpy.

This is how sound waves are filtered out. I realize that I didn't read the question closely enough, though. If you're working with an LES, at that fine of a spatial resolution, you're probably 1) already using the anelastic equations, and 2) properly resolving a lot of the high-frequency oscillations anyway. As @milancurcic mentioned, gravity waves become an issue as the next-highest frequency waves. These notes go through the problem of gravity waves in a shallow-water model, and this article (behind a paywall) shows how a good choice of initialization can help eliminate those waves.


Why does El Niño enhance the jet stream? As quoted over here. This is especially interesting to me because El Niño tends to warm up the climate, and warmer climates are generally associated with weaker jet streams.

  El Niño has other effects further into North America. It tends to enhance the jet stream, creating a wall that prevents Arctic air (and the Polar Vortex) from dipping down to mid-latitudes. East Coast winters are generally drier and warmer during El Niño years, which is probably good news to those still smarting from this recent frigid season. The mild winter has interesting downstream effects, like a boost for the U.S. economy during the Christmas season.

I'm not sure if it's correct to say that El Niño enhances the jet stream, reading NWS
they seem to state that the affect is more of a shift in the position of the jet stream over the Pacific and North America. I guess any enhancement would be due to a greater temperature contrast between the poles and equator.


Melting point of minerals Looking at this website, I can see the melting point of the following minerals: http://hyperphysics.phy-astr.gsu.edu/hbase/Geophys/meltrock.html

Apparently at a temperature of 1200 °C, all the minerals exist in a molten state. But, looking at the melting point for quartz on Wikipedia I find that the melting point is close to 1700 °C. What’s going on here?

It can’t be an effect of the pressure. In the interior, the pressure will be higher and thus further increase the melting point.

Start with my answer to this very highly related question here:

https://earthscience.stackexchange.com/a/2742/725

The melting point of minerals in isolation, or a pure substance is higher than mixtures of minerals. For example - a (well-mixed) mixture of quartz and pyroxene will melt at a lower temperature than pure quartz or pure pyroxene. The exact temperature is dependent on the proportions.

In general, the more different kind of components (i.e. "things": minerals, elements, molecules, etc) you add to a mixture, the lower the melting point of the mixture as a whole.

For example, sea water melts at -2 °C because it's a mixture of water (melts at 0 °C) and all kinds of salts. Steel melts at a lower temperature than pure iron because it's a mixture of iron and carbon.


Why is diabase/dolerite erosion resistant? Diabase is often considered to be relatively erosion resistant. We can generally find diabase dikes cross-cutting various strata through the geologic landscape, wherein, the diabase dike or sill is rather intact as a structure while the surrounding strata have eroded around it.

Of course, differential weathering and erosion depend on the relative susceptibility among those rocks which are next to each other, but if you take into consideration the Goldich Dissolution Series, shouldn't diabase and other basalt-like bodies be easily eroded? Basalts and diabase have calcium plagioclase as a major mineral component.

I could see how shales could weather and erode much more quickly but it seems unlikely that a limestone in an arid environment or a mature quartz arenite sandstone ever would do so.

Diabase is indeed relatively erosion resistant. One of the reasons is grain size.

Consider the two other chemical equivalent of diabase: gabbro (coarse-grained) and basalt (glassy and fine-grained), which should potentially be similarly erosion resistant. This is not the case. Both gabbro and basalt erode very easily.

Coarse-grained rocks, in general, erode easily. The large grain size means it is relatively easy to dislodge individual grains out of the rock. You don't need a lot of water, for instance, to percolate between the not-many grain boundaries and release them. To make it worse, gabbros are full of minerals that are easily erodable such as plagioclase and olivine.

On the other hand, basalts also tend to weather easily. Having cooled fast, they are usually glassy which tends to crack during cooling. Glass is general is less resistant to chemical alteration than minerals, under similar conditions. Basalts also contain gas bubbles, which cause weakness spots in which the rock can break, leading to preferred weathering.

Diabase is fine grained, and contains no glass. It does not suffer from these two conditions. It is composed of very fine interlocking crystals that are very hard to dislodge, and is not penetrable by fluids.

That said, remember that it is all relative. A diabase will still weather faster than even the worst type of granite.


How to find molarity of a fertilizer? I want to add nitrogen to my soil samples in a lab as fertilizer (ammonium nitrate). I need to add it equivalent of 50 kgN/ha. I have a clump of soil, that weighs 20g. What is the best way to convert it to concentration, molarity or any other measure of how to prepare my fertilizer solution without knowing density, depth or any similar physical properties of the soil.

Basically, I would like to know if there is a way to convert nitrogen load from kg/ha to 20 gr. Thanks everyone in advance!

You probably mean 50kgN/ha, which sounds like a plausible number for fertilizer application.

I don't know what you are doing exactly, but you need to know how much surface area your soil sample represents. If you do planting tests, it will be simply the surface of your bed.

Then it's just mulitplying the surface area by 50kgN/ha (mind the units!). Now you know how much N you need.

If you buy fertilizer, the fertilizer value in kg/t, kg/m³ or some similar measure will be given for the important macronutrients NPK. Else you need to know how much N is in Ammonia per weight but I think that is the easy part.

Disclaimer: I've never done any soil science, but I worked with farmers for a while and was in a field where fertilizer etc. matters.


If the top layer of a large river freezes, is the remainder's rate of flow affected? This question is a hypothetical, and has to do with the notion of cheap, modular turbines that can be sunk and anchored to a specific depth in a body of water. Each would not generate much power, but collectively that might be different, and it's a possible means of giving people living on riverfront properties greater self sufficiency. But that's not the question, just the context, so it need not be addressed.

The question is: When the topmost layer of a large body of moving water freezes, and assuming the water beneath is insulated by the ice and does not freeze, does that remaining water's rate of flow (rate, not volume) differ from its rate when none of the body of water was frozen?

No, the rate of flow would usually be unaffected. The same volume of water has to get to the sea, so unless the ice was so thick and so well anchored to the riverbank as to exert pressure on the flow of water beneath, which is very unlikely, the rate of flow would remain the same. If, in the unlikely event that the ice exerted pressure on the water, the rate of flow would speed up.

It's the same when you pinch the end of a garden hose. The same volume of water has to get through a narrower opening, so the jet becomes smaller but faster and more powerful because it is under pressure. For your purposes a faster flow would help your turbines to do their job but as I say, icing conditions are rarely such that ice would exert pressure on the flow. The friction between the current and the relatively thin coating of ice which usually occurs would not be enough to have any noticeable effect on the rate of flow.


Were all of Earth's minerals created before Earth's formation, during, or after? I've heard that during the accretion of proto-planets that formed the earth, 60 minerals were created through the reactions of atoms and molecules in the nebula.

Do these first minerals still make up Earth?
Do we have more minerals? If so, how and when were they created?

This is a very interesting topic that in the past several years has reached the spotlight because of the work of Robert Hazen on the concept of "mineral evolution".

Minerals are basically just inorganic compounds: you take several elements, arrange them in certain ways and you get minerals. Thus, the minerals will be mostly determined by what's available to them in term of chemical elements. The solar nebula in Earth's area was composed of very few major elements: Fe, Si, Mg, Al, O, C, etc. There are not many ways you can arrange this limited number of elements, and the mineral diversity was rather small. You cite the number 60. Could be more, could be less, but it sounds about right.

With time, certain processes on Earth (magmatic, metamorphic, metasomatic, hydrothermal, you name it) preferentially move certain elements relative to others. This causes redistribution of elements in space, enriching and depleting certain elements in various areas. Elements that were previously present only in trace amounts and did not form minerals of their own may be concentrated so that they do.

This is a time dependent process, hence the name "mineral evolution". This is a nice figure that sums things up:

You can read more about it in Hazen's website (from which I took the above figure): http://hazen.carnegiescience.edu/research/mineral-evolution

Be careful though when searching for "mineral evolution" online - you usually get more results of pseudoscientific and creationist websites rather than actual science.


Was the Southern Hemisphere cooler during the Younger Dryas? The Younger Dryas was associated with less heat transport from the Southern Hemisphere to the Northern Hemisphere (and a lower flux of the meridional overturning circulation).

So I'm wondering - what were the temperatures of the SH like during that period?

According to the paper The Younger Dryas Climate Event (Carlson, 2013), the Younger Dryas was a period of warming in the extratropical Southern Hemisphere, and quite a significant one as well, as, from the article:

  SST records show a warming
  of 0.3–1.9 C from the southeast Atlantic to New Zealand

This is supported by

  Speleothem and pollen records from New Zealand and
  pollen records from South America confirm that the Younger
  Dryas was generally a period of warming

Citing glacial retreat measured in places such as New Zealand, the article Glacier retreat in New Zealand during the Younger Dryas stadial (Kaplan et al. 2010) hypothesised the mechanism to be due to

  extensive winter sea ice and curtailed meridional ocean overturning in the North Atlantic led to a strong interhemispheric thermal gradient8 during late-glacial times, in turn leading to increased upwelling and $\mathrm{CO}_2$ release from the Southern Ocean, thereby triggering Southern Hemisphere warming during the northern Younger Dryas.

Research on the deposits observed in the Australian tropics of the age in the article Late Pleistocene record of cyclic eolian activity from tropical Australia suggesting the Younger Dryas is not an unusual climatic event (De Deckker et al. 1991) revealed that the southern tropics experienced a drying phase, that fits well with a cyclic climatic phase.

In terms of the southern tropics, the article Tropical climates in the game of two hemispheres revealed by
abrupt climatic change (Ledru et al. 2002) suggest that there were significant variations, as recorded in pollen samples from the South American tropics, leading the authors to suggest that

  One consequence of the Younger Dryas changes would be the location of the Intratropical Convergence Zone in a southern position, so that even tropical regions would have been under Arctic influence.


Comparing the Earth with a basketball? (Please excuse my English)

The picture above is an image of the Earth without water. We know that the picture above is a very exaggerated one. If the Earth were to shrink to the size of the picture above, it would look like a very smooth and round sphere.
I wonder what it would look like if the Earth were reduced to the size of a basketball. I think the basketball-sized Earth will have a smoother surface than actual basketball. But I don't know how smooth the surface of the basketball-sized Earth will be.
What would the basketball-sized Earth look like? Will it look like a marble ball with shinning smooth surface?

The picture above is an image of the Earth without water. We know that the picture above is a very exaggerated one.

About the image
The image shown in the question is very widely claimed to show an image of the Earth without water. A google image search for that image comes up with the nice logo "earth form without water". This image pops up on the internet again and again. This image does not show an Earth without water, even greatly exaggerated.
Note that the image in the question has a fuzzed out title and legend. All of the images on the internet that claim this is an image of the Earth without water have a fuzzed out title and legend. A non-spinning version of that image with the correct title and legend is below. (I do not like reading around animated gifs.)

Source: Bezděk, Aleš, and Josef Sebera. "Matlab script for 3D visualizing geodata on a rotating globe." Computers & Geosciences 56 (2013): 127-130.
The above image is by the author of the image. The title says what the image shows, which is the geoid height. The legend shows that the variation from the highest to lowest geoid height is less than 200 meters. Compare that with the 19.777 km altitude difference between the highest mountain above sea level and the deepest trench below sea level. The image shows the Tibetan plateau as below the reference ellipsoid and shows the Java trench (not visible in the above) as above the reference ellipsoid. This is not a world without oceans.
So what does the image show?
What the image does show is the geoid height, greatly exaggerated. A geoid is the surface of constant gravitational plus centrifugal potential that is the best fit in a least squares sense to mean sea level. In other words, the geoid portrays the Earth's gravitational field. A simpler model of mean sea level is an oblate ellipsoid. The geoid height at some point is the altitude difference between corresponding points on the geoid and on the reference ellipsoid. Red areas in the image (positive geoid height) show where the geoid is above the reference ellipsoid while blue areas (negative geoid height) show where the geoid is below the reference ellipsoid.
Look at the Tibet Plateau in the rotating image. It is blue, which means the geoid there is below the reference ellipsoid. Look at the Java Trench. It is the very deepest red, which means the geoid there is above the reference ellipsoid. The Rocky Mountains are white, no different than the rest of the northern North America. The Alps are reddish, but not as red as is the eastern North Atlantic.
The Tibet Plateau shows up as a depression in this image instead of as a large elevated land mass because it is are very large blob of rock that has less than average density. The rock near the Java Trench is very dense basalt.
The image does not represent anything close to what many say it represents. The image does represent what the author intended it to represent. The image is interesting to those who know what the image portrays. The problem is that the image is so often described incorrectly.
An image of the Earth without water
An image of the Earth without water, slightly exaggerated, is shown below.

Source: : Jack Cook, Woods Hole Oceanographic Institution, Howard Perlman, USGS, Astronomy Picture of the Day 2012 May 15
The Earth is round rather than lumpy. For portrayal purposes, a ball of water about 1365 km across hovers over the western US. That ball represents all of the Earth's surface waters.

Comparison to a basketball
The difference between the Earth's equatorial  and polar radii is about 21.385 km. The difference between the height of the highest mountain on the Earth above sea level and the Earth's deepest trench below sea level is about 19.777 km.
Shrinking these down to the size of a basketball with a circumference of 29.5 inches (I used US units because that is what NBA regulations specify) shrinks the 21.385 km difference between the equatorial and polar radii to a mere 0.4 millimeters, shrinks the height of Mount Everest above sea level to 0.165 millimeters, and shrinks the depth Challenger Deep below sea level to 0.204 millimeters.
A basketball is supposed to be round(ish). That the Earth's polar radius is 99.66% of its equatorial radius makes the Earth fairly "roundish". The roundness of the Earth is in line with the roundness of a billiard ball, and probably for a basketball. (While I did find roundness requirements for a billiard ball, I was unsuccessful in finding roundness requirements for a basketball.)
What about smoothness? Basketballs are not smooth. They are intentionally designed with stipples (pebbles) and channels so as to make basketballs easier to handle. There are patents galore regarding the size, shape, and placement of those pebbles and channels. The pebbles are a bit higher than 0.17 millimeters and the channels are significantly deeper than 0.2 millimeters. The Earth is smoother than a basketball because basketballs are unsmooth by design.


How will climate change affect the extent of the Sahara Desert? Will it increase the size of it? (perhaps by increasing the extent of the Hadley Cells)?

Based on past  events supported by archeological finds  climate change will affect it.
Here is a short quote from an about.com referencing National Geographic

  "... recent excavations at the site of Gobero in Niger indicate.
  Gobero is a cemetery site, including at least 200 human burials
  located on top of a ridge or set of ridges, sand dunes with a hard
  calcrete-fringe. These burials occurred in two periods of settlement:
  7700-6200 BC (called Kiffian culture) and 5200-2500 BC (called
  Tenerean culture).

  There, explorations by a team led by National Geographic
  Explorer-in-Residence and University of Chicago paleontologist Paul C.
  Sereno, have illuminated some small part of the last 10,000 years of
  the Saharan ecosystem. "

Some references cited include

Sereno, Paul C., et al. 2008 Lakeside Cemeteries in the Sahara: 5000
Years of Holocene Population and Environmental Change. PLoS ONE
3(8):e2995. Free article download available

Green Sahara: National Geographic Magazine September 2008.

  People lived on the edge of the desert thousands of years ago since
  the last ice age. The Sahara was then a much wetter place than it is
  today. Over 30,000 petroglyphs of river animals such as crocodiles[8]
  survive, with half found in the Tassili n'Ajjer in southeast
  Algeria.... The modern Sahara, though, is not lush in vegetation,
  except in the Nile Valley, at a few oases, and in the northern
  highlands, where Mediterranean plants such as the olive tree are found
  to grow. The region has been this way since about 1600 BCE (aka as BC
  or  4600 years ago), after shifts in the Earth's axis increased
  temperatures and decreased precipitation.

Citation: American Geophysical Union. "Sahara's Abrupt
Desertification Started By Changes In Earth's Orbit, Accelerated By
Atmospheric And Vegetation Feedbacks." ScienceDaily. ScienceDaily, 12
July 1999. .

Within the Holocene era, roughly the last 11,000 years, meaning the time frame within which the Western Sahara was sufficiently habitable to support a cemetery,  we have experienced a  "little ice age". The Holocene is generally described as beginning at the end of the last glacial period.

There has been climate variation during the Holocene such that the Sahara could support human life. It is fair to extrapolate that any future climate change will also affect the Sahara.

If you look at a world map you will see that the great dry deserts pretty much center  around the 20 degree latitude. Proximity to a large body of water does not mean that the  landmass can support vegetation. The Namib desert is south Africa stretches to the Atlantic coast as  the Atacama in Chile   has a very long boundary  with the Pacific. (The Atacama is also the driest place on earth)

If there is going to be an affect on a desert from climate change , it is possible that the  desert could extend to a major coast line. It would be reasonable that the North African coast could undergo desertification. It  is already  coterminous with the east and west coastal borders of Africa. If it expands , it can go north  or south. It already approaches the Mediterranean coast

Presumably the Sahel could also become drier and there is some indication this has been happening. The Sahel is where we find the great savannahs. That  region is most at risk for increased temperature or aridity. Movement in eh other direction- milder temperatures and  more  rain would be beneficial  and supportive of  animals and human habitation.

The arid region and the mean temperatures in the western Sahara are sufficiently  large that it seems unlikely the Hadley cell formation would be affected, but I am not a meteorologist, so I defer to  them on that point.


What caused the high temperatures that resulted in the Cretaceous aged komatiite lavas of Gorgona Island, Colombia? According to the San Diego State University (SDSU) webpage Unusual Lava Types, komatiite is

  ultramafic volcanic rocks, having very low silica contents (~40-45%) and very high $\ce{MgO}$ contents (~18%). These lavas are exceptional not only for their compositions, but also for their very old, restricted ages. These lavas have no modern analogs.

The vast majority of komatiite deposits, according to the SDSU webpage, are about 3 Ga or older in age, due to

  These ancient lava flows erupted at a time when the Earth's internal heat was much greater than today, thus generating exceptionally hot, fluid lavas with calculated eruption temperatures in excess of 1,600 degrees C (2,900 degrees F).

However, with many things in Science, there are exceptions - according to the SDSU webpage, Gorgona Island, Colombia has komatiite deposits that are Cretaceous in age (around 90 Ma). The presence of young komatiite hints at higher temperatures at formation - something that has not been seen in any significant amount since the Archaean.

What caused the high temperatures that resulted in the Cretaceous aged komatiite lavas of Gorgona Island, Colombia?

There are some suggestions in the recent paper in Nature Geoscience about Tortugal lava suite. The suite formed 89 million years ago and recorded mantle temperatures as high as Archaean komatiites. The mantle plumes are chemically and thermally heterogeneous, so the researchers interpret these rocks as the result of melting the hot core of the plume head that produced the Caribbean large igneous province. Their results imply that a mantle reservoir as hot as those responsible for some Archaean lavas has survived eons of convection in the deep Earth and is still being tapped by mantle plumes.

The link: https://www.nature.com/ngeo/journal/v10/n6/full/ngeo2954.html


How is known that the Earth core is solid? As I know, the Earth core is solid. This is known, because it conducts also transverse waves, while liquids conduct only longitudinal waves.

But, how was it found? The inner core is in the outer core, which is liquid. Thus, it is inpermeable for the transverse waves.

So, any transverse wave originating in the crust, shouldn't be able to reach the core and vice versa.

How could it been measured?

This is a good observation. The reason we can measure S-wave (transverse) propagation in the inner core is because P-waves can set up S-waves and vice versa. When an S-wave hits the mantle-outer core boundary at an angle it has a vibration component which is normal to the boundary and this sets up a P-wave in the liquid inner core. The opposite happens at the outer core-inner core boundary, where the P-wave at the boundary sets up an S-wave in the solid inner core. In this way, we can measure S-wave transmission through the inner core.


Does finer input data lead to a better model accuracy? Climate and chemical transport models all need input files including meteorology data, emission inventory, surface data (e.g. land use type) to initialize the simulation field.

Take the case of WRF for regional meteorology data. I always use the 1 degree x 1 degree NCEP FNL analysis data in grib2 format as the model-driven data supporting the boundary and initial information every 6 hours.

Now, I find that the ECMWF interim reanalysis data with spatial 0.25 degree x 0.25 degree can also be used as the WRF input.

So, I'm wondering does finer input data induce better model performance? How much can be improved if the answer is true?

I have thought out some pros of finer input file (in both spatial and temporal scale):

pros: (1) finer input field means the physical process of smaller scale(e.g turbulent transport of water) can be depicted better in some extent; (2) finer temporal variation of chemical species can emulate the real emission process better(e.g, emission related to traffic in a day).

Are there any cons that come with the performance improvement except the computational cost?

My experience with the WRF is in tropical cyclone simulation, but here are some things to consider:

Smaller scale information might already be resolved in your higher resolution initial conditions, improving the spin-up time of effects you are trying to model
I believe the ECMWF resolution has been statistically downscaled from a 0.75 degree dataset, I might be wrong on that one though. That would mean that your 0.25 degree is just some interpolated data
Some models perform better than others in specific regions
Higher resolution is not better if your parameterization is wrong
You can find caveats of specific datasets here: PSD Gridded Climate Datasets.


Bifurcation Scenarios in the Atmosphere I read that the mathematical definition of bifurcation is that, at a critical value of a parameter that governs the dynamical system, the system changes to a topologically different system than the previous.

I recognize that the scope of this question is large, but still:

Why is this phenomenon relevant in atmospheric sciences?

One can say that since the topology of the systems orbit changed, so different, previously unaccounted for, phenomena will show up - but that is obvious.

Why, if at all, should a climatologist trying to predict a long term time series keep bifurcation in mind, given they can also compute the results numerically within the limits of precision, without explicitly worrying about bifurcation?

Bifurcation is relevant to the atmosphere because it effects the predictability of the evolution of the atmosphere.  I can't speak to its merits with climate models, but with weather it is quite important.  As Lorenz discovered, the atmosphere is chaotic and numerical prediction is very sensitive to the initial conditions.  If the initial conditions are close to the bifurcation point, the solution can be wildly different and this leads to poor predictability.

In particular, weather models assimilate observations and each observation is a value and an uncertainty.  When the uncertainty contains the bifurcation point that essentially means for that particular scenario the instrument error is such that the instrument can provide an observation on either side of the bifurcation. This can be explored with ensemble modeling where many models are run with slightly different initial conditions (or slightly different models, or both) and leads to a large spread in the ensemble and low confidence in the forecast.  Our atmosphere can be more sensitive than the instruments we use to measure it can detect.

I'll try to dig up some references to presentations I've seen that show hurricane dissapation / rapid strengthening determined by differences in gulf of Mexico observations smaller than the instrument error reporting them.  For more NWP-centric studies, you can search for articles on predictability.  I know there should be a couple by Dr. Fuqing Zhang (PSU) and I'll edit when I can point to some specific articles.


The Dust Bowl: how's the recovery of all that topsoil going? I must admit this is very confusing: one gets different types of information from different sources that often contradict one another.

It is said that the main event occurred due to some misguided farming practices; that those practices allowed strong winds to pick up all the topsoil in the area, carry it east, and deposit it into the Atlantic, more or less in toto. This may not be altogether accurate, but that's the impression one gets from reading essays, articles, and the Wikipedia entry pertaining to the Dust Bowl.

That said, various sources maintain that it takes 500 years or more to form 1 inch of topsoil.

Others suggest coyly that the process can be expedited artificially by burying lots of organic garbage and introducing earthworms to it: a few years of this, and voila - we've got topsoil! (Few of them say how many years, exactly, and where's one supposed to get all those earthworms?)

Still other sources say that because of soil erosion and other intriguing factors we may be running out of arable land. And not just in Kansas either, but all over the globe.

With all of the above in mind, my question is: have the areas affected by the Dust Bowl recovered partially, completely, or not at all? How much of that land can be classified as arable once again?

Soil is not a single uniform material. When wind erosion takes hold, as in the infamous 'dust bowl' of the 1930s, it is the uppermost A horizon, and to a lesser extent the B horizon that is mainly lost. The lost soil takes with it most of the organic carbon, and it is this component that is most difficult to replace.

Rates of soil loss are primarily factors of the underlying geology and climate. A wetter and warm climate will regenerate soil very much faster than a cold dry climate. Basalt and shale will break down into soil much faster than, for example, quartzite or granite. The rate of rock weathering is not quite the same as the rate of soil formation.

As a first approximation, rate of weathering = rate of soil formation + rate of mass loss by various processes.
Wakatsuki and Rasyudin (1992) Rates of Weathering and Soil Formation, Geoderma, V52, 3-4, gives a good assessment of such rates.
Their best estimate is that soil formation occurs at 370–1290 (mean = 700) kg ha−1 yr−1. Or roughly 0.3 mm per decade, excluding the lost organic carbon. This is equivalent to about 3 mm of new soil generation since the great dust bowl.

In other words, no, the lost soil is nowhere near replenished yet. Moreover, the impact of climate change does not bode well for the future of soil regeneration.


Hydrology - Flowrate vs. radius of influence I am using the following equation (Theim/Dupuit) to determine radius of influence for an unconfined aquifer.

Assume all other variables are held constant. If Q increases, does the Radius of influence R2 also increase?

I think your confusion is because you are keeping the head fixed. As you increase Q at fixed T the drawdown curve will become steeper when r1, h1, and h2 are fixed. That means r2 will move towards the pumping well.


Should I cut buildings from flood zone? At work (at a local authority), I have a flood zone plan/layer that was created using ArcGIS software from an Elevation Model which has an accuracy of 1 metre. The zone covers an urban area. I cannot get to the resolution of which buildings will be more effected by flood and which won't, but I do have a layer of all buildings.

The question is, whether I should cut the buildings out of the flood zone, and why?

Since it is not known at this stage what measures were implemented for each building in regard with eventual flood situations, it does not make sense to purposely exclude the buildings from the flood zone until some kind of general database with that info is built and put into context in your system. Even-more, a lot could happen in a potential flood zone when an extreme event occurs, regardless of the prevention plan by various owners and city management. A risk management model with proper risk probabilities, potential damages and monetary values would eventually be required to better represent the up-to-date context of the site.


What is hydraulic diffusivity? I'm having trouble grasping which properties of the soil are described by hydraulic diffusivity (sometimes referred to as soil-moisture diffusivity which is based on the diffusion equation).

What does hydraulic diffusivity describe and how is it related to hydraulic conductivity?

So let's get through some definitions. I will not discuss the derivations of this, but you can look this up, if you want to in the source I provided.

We find ourselves in a porous medium, so we will have always some volume filling factor $\Theta$ of water in rock.

 (copyright K. Roth, Heidelberg University)

Then we can start with the hydraulic conductivity, that can be defined as $$K = \frac{\Theta \left< \kappa \nabla P \right>}{\mu}$$
Here we have quite some notation to clarify:

$\Theta$ is the local filling fraction of water in an aquifer
$\mu$ is the molecular diffusivity
The brackets $\left< ... \right>$ denote the macroscopic average over the microscopic aquifer structure that is given by Permeability $\kappa$ and local pressure gradient.
The Permeability itself is a way to encapsulate how easy water can flow through a given geometry.

This is the most rigorous definition I could find in my notes. The lecture however is unavailable on the internet, so you'll have to take my word for this. I just mention this as I don't know whether you're more interested on the theoretical side, or the pragmatic.

The pragmatic approach is usually to simply state Darcy's law $$ q = - K \frac{dh}{dz} $$
relating the water mass flux $q = \rho v$ to the gradient of hydraulic head.
We see now that a flow velocity will result from a pressure gradient, but strongly modified through the microscopic properties encapsulated in K.

The hydraulic diffusivity you search for is then written as $$D = \frac{K}{S}$$ where S is the water storage fraction, with $S \sim \Theta$ (not strictly correct). So in a way this just cancels the $\Theta$ in $K$ and appears in the pressure diffusion equation:
$$ \partial_t P = D \Delta P $$
and therefore D gives the local diffusion speed of pressure disturbances (which is interestingly not the speed of sound $c_s$ in aquifers!)

We could now say, for educatory purposes that the difference between $K$ and $D$ is only $\Theta$. This makes sense when we look at the context where those two parameters appear:
$K$ gives the strength of mass-flow for the fluid, the better filled the volume is, the more mass will flow.

Pressure, however does not care for the amount of water that flows, as it exists everywhere where water is sitting at a given moment, so the coefficient for its propagation does not depend on the filling of volume.

You can look up the derivations for example here.
Tell me in the comments, if anything is unclear.


What wind speeds and gusts can usually damage houses or trees? In my region wind speed increases in Autumn. It reaches to 30 km/h with gust up to 40 km/h. I want to know at which speed the wind and gust can cause small damage of the normal window glass (We mostly using PVC for the windows).

Tonight I noticed that ventilizer fan damaged due to opposite wind direction and cause to stop. Also, my TV Antenna was completely damaged (I couldn't find it).

I want to know Wind and Gust speeds that can cause damage to usual house, outdoor cables, antenna, windows?

Thanks

This Beaufort Scale of Wind Force image may help answer your question.

source: strleng.blogspot.com


What is this upside-down transparent funnel-shaped item on a weather monitoring station? I snapped this photo with my cell phone at a fishing harbor in northern Taiwan.

I recognize a weather vane + anemometer, and I suspect the two large, dark things at the tops of poles might be omnidirectional antennas, but the item near the right shaped like an upside-down funnel at the end of a u-shaped tube appeared to be transparent, as if glass or plastic.

I've never seen anything like it. Any thoughts?

The upside down funnel-shaped attachment is used to prevent rain-water getting inside the inlet of instruments which analyse ambient air. Usually, these instruments are air pollution monitoring instruments like ozone analyser, carbon monoxide analyser, particle concentration analyser, etc.

One may think why can't just use U-shaped tube (inlet). In case of U-shaped inlet, rainwater falling on inlet will eventually roll down near the entrance and will be pulled in with air due to strong flow. Funnel shape will have smaller flow near the wider opening and prevents water from getting into the air stream.


Why does Colorado have so many days of partly cloudy skies? So I found a map of the US that shows the number of partly cloudy days in different areas of the country. It seems that Florida is the only state with more partly cloudy days. Partly cloudy days are when the sky is not clear but not overcast. I think it's odd that Colorado has few days of clear skies yet few days of overcast skies.

http://www.eldoradocountyweather.com/climate/US%20Climate%20Maps/images/Lower%2048%20States/Sky%20Cover%20-%20Visibility/Mean%20Number%20of%20Partly%20Cloudy%20Days%20Sunrise%20to%20Sunrise/Annual%20Mean%20Number%20of%20Partly%20Cloudy%20Days%20Sunrise%20to%20Sunset.png

I'd personally theorize the main answer is mountains.

Thinking the key is localized "mesoscale" features that disrupt large-scale patterns.

In Florida, it's the surrounding oceans that generate daytime seabreeze storms in the summer and provide moisture return (and thus clouds) more quickly after frontal passages in the winter.

In Colorado, would think it's the mountainous terrain causing circulations like the Denver Cyclone creating daytime clouds in the summer, and vertical motion that may well disrupt some of the foggy, stable winter days.

Some mesoscale influences create almost continual cloudiness, such as the cold oceans along the US Pacific Coast.  Others remove more cloudiness, like the terrain and resulting deserts in much of the US Southwest.  For more in-depth details on cloudcoverage, including annual and monthly totals maps, see this us-climate.blogspot.com article.

But one key aspect of both the Florida seabreeze and mountainous circulations is that they are diurnal.  So, often, whatever they bring in one part of the day, they generate the opposite at the other end of the day.  For more about how mountain winds vary through the day, look into mountain and valley breezes.

I'd also suggest the greatest proportion of cloudiest (foggiest) days in the US are winter periods where cold air is in place under a high pressure and moisture builds up within the stable conditions.  Being elevated in Colorado may additionally aid prevention of some such days by having lower moisture depth and also by being more exposed to winds [while Florida instead is less often deeply entrenched in cold airmasses].

Just thoughts, but hopefully that offers some direction for why it may be!


Why don't tropical cyclones hit Hawaii? Image by Wikipedia user Citynoise using NOAA data.

If you look at this map of tropical cyclone tracks, you'll notice that there's a gap in the central northern Pacific where tropical cyclones generally don't go.  On the edge of that gap, you'll find the Hawaiian Islands.  Further, although the islands are sort of in the fringes of the eastern tropical cyclone area, they're in a gap in that fringe -- tropical cyclones forming in the eastern Pacific track either north or south of them.

So, why don't tropical cyclones hit Hawaii?

There are at least two effects are at play here:

Hawaii is a tiny speck of land in a big ocean; the odds of hitting any given spot in the Pacific with a storm in any given season is incredibly low.
The waters around Hawaii are deep and generally relatively cold, they suck energy out of storm systems that pass over them causing cyclones to downgrade into tropical depressions before they hit the islands, so there are plenty of storms but few are still cyclone grade when they arrive.


Should RTM amplitudes (pre- and post-stack) be related to rock physics properties? There is as huge uplift in the image quality near and under salt bodies (e.g. the Middle Miocene of the deepwater Gulf of Mexico) by using reverse time migration or RTM. We can now make accurate structural interpretation in places we couldn't just see using Kirchhoff migration.

Is it proper to make an amplitude analysis of RTM migrated data to invert for reservoir properties? Do RTM horizon amplitudes relate to rock properties?

I figure this will depend on the specific vendor implementation of RTM, and the assumptions made to speed up the code, but in its pure form is RTM amplitude friendly?

Please help. Any literature on the subject is much appreciated.

It depends on the implementation, but bare-bones reverse time migration is usually not amplitude friendly.

The problem is that the ideal imaging condition — deconvolution — is difficult to apply or unstable in the time domain. So cross-correlation is used instead, and this loses the relative amplitude information... so amplitudes are no longer necessarily related to reflection coefficients. Since this is the assumption of inversion (e.g. AVO inversion), an RTM volume could give spurious results.

Several vendors have devised ways to compensate for this. For example, Zhang & Sun (2009; First Break, vol 26) described one of CGG's methods. Zhang et al. have written more recently (2013; EAGE London) about how this processing has evolved. Cogan et al (2011; SEG Annual Meeting) have written about Schlumberger's normalization algorithms, which aim to achieve the same goal.

Those papers also contain nice explanations of why RTM is not ordinarily amplitude friendly.

My advice is to talk at length to your vendor about the processing applied, and the attention paid to phase and amplitude. Ask lots of questions. Most processors (generalizing horribly) are either over-enthusiatic about treatments, especially new and/or proprietary ones, or ignorant of your needs as an interpreter–analyst. So if you're actually in the middle of processing now, test everything, ideally against geological data (like a synthetic).

Lots of processing steps can hurt amplitudes, and inversion can work in surprisingly poor data... so assume nothing, and never give up!


Why are there so many "horseshoe lakes", and are they really mostly in the US? The Wikipedia disambiguation page Horseshoe lake lists 31 of them, of which 27 are in the US.
Question:

Why are there so many "horseshoe lakes"? Are they usually horseshoe-shaped, (or full of horseshoes)?
Are they really mostly in the US? For geological reasons, or are they everywhere but not necessarily named as such (I've made attempts to search google translated "horseshoe lake" in Spanish and Chinese but did not find many)

Let's get rid of a red herring from the Wikipedia page linked to in the question!
Horseshoe Lake, Arizona - is a reservoir created by the Horseshoe dam. Nothing to do with actual horseshoe lakes.
The other's lakes listed might be genuine horseshoe lakes, but there are no aerial photographs of them.
Generally, horseshoe lakes are oxbow lakes which is

a U-shaped lake that forms when a wide meander of a river is cut off, creating a free-standing body of water. In south Texas, oxbows left by the Rio Grande are called resacas. In Australia, oxbow lakes are called billabongs.

A resacas is a

former channels of the Rio Grande and are naturally cut off from the river, having no inlet or outlet
The Rio Grande's water moves from Colorado to the Gulf of Mexico. Every year during spring, melted snow would flow into the Rio Grande, bringing seasonal flood waters to the most southern tip of Texas.3 Given the overflow of the river's main and distributary channel banks, the Rio Grande would carve new river channels, resacas. When the seasonal flooding ended and the river retreated, the newly formed oxbow lakes remained, creating resacas all throughout the Rio Grande Valley.

In Australia, billabongs are

an isolated pond left behind after a river changes course. Billabongs are usually formed when the path of a creek or river changes, leaving the former branch with a dead end. As a result of the arid Australian climate in which these "dead rivers" are often found, billabongs fill with water seasonally but can be dry for a greater part of the year.
Billabongs were significant because they held water longer than parts of rivers; it was important for people to identify and name these areas.

In general,

An oxbow lake forms when a river creates a meander, due to the river's eroding bank. After a long period of time, the meander becomes very curved, and eventually the neck of the meander becomes narrower and the river cuts through the neck, cutting off the meander and forming an oxbow lake.
When a river reaches a low-lying plain, often in its final course to the sea or a lake, it meanders widely. In the vicinity of a river bend, deposition occurs on the convex bank (the bank with the smaller radius). In contrast, both lateral erosion and undercutting occur on the cut bank or concave bank (the bank with the greater radius). Continuous deposition on the convex bank and erosion of the concave bank of a meandering river cause the formation of a very pronounced meander with two concave banks getting closer. The narrow neck of land between the two neighboring concave banks is finally cut through, either by lateral erosion of the two concave banks or by the strong currents of a flood. When this happens a new, straighter river channel develops—and an abandoned meander loop, called a cutoff, forms. When deposition finally seals off the cutoff from the river channel, an oxbow lake forms. This process can occur over a time from a few years to several decades, and may sometimes become essentially static.

The naming of oxbow lakes as horseshoe lakes might just be American terminology. When such lakes were being "discovered" and "mapped" most people would have known about horseshoes, but maybe not oxbows so they got named after what they knew.
Oxbow lakes do exist elsewhere, they're just not named as horseshoe lakes.
The Kanwar Lake Bird Sanctuary in India is one of the largest oxbow lakes in Asia. In the United Kingdom they also occur in the Gower Peninsular of southwest Wales and there is also the Bole and Burton Round in Nottinghamshire.
Celestially, there has also been a possible oxbow lake postulated in Saraswati Flumen near Ontario Lacus on Saturn's moon Titan.


Nuclear testing and the Anthropocene, a chemostratigraphic link? The Anthropocene is described by Wikipedia as

  an informal geologic chronological term for the proposed epoch that began when human activities had a significant global impact on the Earth's ecosystems

Due to its informal nature and not wide acceptance, there is often debate among adherents about when the Anthropocene is supposed to have started. Note: this is not a question about this debate.

A working group recently suggested in the article When did the Anthropocene begin? A mid-twentieth century boundary level is stratigraphically optimal (Zalasiewicz et al. 2015) has taken a different approach, looking at the effects of human activity on the chemostratigraphic record, suggesting that the Anthropocene epoch ought to have its beginning at the start of the nuclear age.

From the article's abstract:

  We propose an appropriate boundary level here to be the time of the world's first nuclear bomb explosion, on July 16th 1945 at Alamogordo, New Mexico; additional bombs were detonated at the average rate of one every 9.6 days until 1988 with attendant worldwide fallout easily identifiable in the chemostratigraphic record. Hence, Anthropocene deposits would be those that may include the globally distributed primary artificial radionuclide signal, while also being recognized using a wide range of other stratigraphic criteria.

This question is not about the legitimacy of such an epoch, but looks at the main point of the working group's findings that radionuclides from nuclear testing may have a measurable imprint on the chemostratigraphic record.

Have there been any global correlation of artificial radionuclides in the recent chemstratigraphic record to justify this as a possible Holocene-Anthropocene boundary?

Certain anthropogenic radionuclides have pragmatic advantages as markers for the start of the anthropocene.  The advantage of radionuclides over anthropogenic stable nuclides is that the primordial production of all but a few radionuclides has decayed away.  Thus natural background levels are below detection by sensitive analytical methods except in a few localized situations (e.g. preCambrian natural reactors). Even radionuclides that occur naturally may be useful if the anthropogenic signal is much greater than the natural levels.

An important trait for a radionuclide stratigraphic marker is that it has a long enough half-life so the anthropogenic material remains in the sediments.

The other important trait for a stratigraphic marker is that it is not redistributed significantly in the environment and stays with the sediments of the proper age. For this reason, 137Cs and plutonium isotopes are valuable since they have low solubility/strong adsorption onto sediments and were distributed widely by atmospheric nuclear testing. However, this does not mean that the radionuclides won't be redistributed. The radionuclides can be redistributed by water, wind, or mass wasting. Colloidal transport can move particles even in saturated groundwater and the vadose zone. In fact, levels of atmospheric fallout plutonium have been used to interpret the redistribution of other contaminants.

Even with very low solubility there can be some dissolved transport into earlier or later sediments. For example, Woods Hole Oceanographic Institute has determined that plutonium mobility may be affected by oxidation state and that more mobile 244Cm can be transported before it decays to 240Pu.

Thus, anthropogenic radionuclides, particularly those of long half life and low solubility that were produced during atmospheric testing, have properties that make for pretty good stratigraphic markers. But the limitations should be kept in mind.

An additional problem is in defining the 'start' of the anthropocene as fitting your marker rather than the other way around.  Certainly, people have had had geologic and ecological impacts before 1945. There are indications that aboriginal burning regimes over 20,000 years caused nutrient export with effects that can be seen in current groundwater chemistry.


What were the tallest mountain ranges in Earth's geological past? There have been numerous episodes of mountain building in Earth's geological history, particularly through the super-continent cycle.  Many mountains and mountain ranges have been eroded, as mentioned in the similar question Determining the paleoelevation of mountain ranges.

What are believed to be the tallest mountain ranges in Earth's geological past?  Additionally, what evidence is there to support these palaeoelevations?

Factors determining the maximum possible height of mountains include the rate of uplift versus the rate of erosion[a] and rock strength.

Rock strength is controlled by the type and internal structure of the rock in question. There is some evidence that once mountains extend above the snow line, glacial and periglacial erosion have a stronger control than exhumation/uplift rate (Brozovic et al, 1997; Egholm et al, 2009).

Everest and the Himalaya have reached their maximum possible elevation: the formation of the Tibetan plateau is due to the failure of rocks preventing the maintenance of discrete mountain peaks. The principle of uniformitarianism suggests that - subject to differences in variables discussed by Egholm et al, including crustal composition - the Himalaya and Tibetan plateau are an excellent approximation to the maximum achievable height of mountain ranges. However, identifying which specific palaeoranges were tallest (as opposed to calculating a plausible upper limit on height) is a significantly harder problem to solve.

[a] Though note that the rate of erosion increases as the rate of uplift increases - for more on erosional equilibrium, see e.g. Riebe et al (2000).


How does a subduction zone form mountains? When an oceanic plate goes under a continental plate are the resulting mountains a result of the melting and then rising of the oceanic plate or are the mountains formed by the continental plate being pushed up by the collision with the oceanic plate?

When the subducted oceanic plate slides beneath the continental crust, it causes crustal thickening and sometimes crustal folding. In addition to this, rising plumes of magma are created when the oceanic plate is forced down into the mantle. These are all orogenic (mountain building) processes. We also have mountains built by volcanoes.

The friction created by the subducted plate scraping the bottom of the plate above as it is being forced into the hot mantle, plus the fact that it carries water with it, causes partial melting of the plate and the rising of magma plumes, which also have a lifting effect. Water mixing with the magma makes it more fluid and more easily melted. These plumes often force their way through the upper plate and erupt as volcanoes, which is why volcanic arcs are created near subduction zones. The classic example of volcanic arcs is found around the Pacific rim, where subduction zones abound.


Hydrophone/geophone sensor sensitivity - noise versus signal Today, a student asked this question, and I was unable to answer it in a way that was understandable and accurate - even for myself.

For seismic data acquisition projects, how are sensors able to 'hear' seismic signals in the midst of other much 'louder' and more ongoing ambient noise sources/events?

I understand that the spectral characteristics of ambient noise - be it a culmination of periodic and/or transient events - are usually much different than downgoing/upcoming recorded seismic signals (~15 - 100Hz). However, I'm unsure if the ability to 'see' through the noise at the acquisition stage is more of a sensor specification topic than some pre-processing filtering/gain action occurring real-time during recording.

Sound-generating wave action takes place only on the surface, and is usually less noisy than seismic events. I'm not sure what you mean by streamers, but I take it you mean ships. These man made sounds are loudest on the surface and much quieter in the depths, whereas  with seismic noises it is the other way around, so the way to preferentially hear seismic sounds is to send your hydrophones deep. In any case, man made sounds are usually very transient and would not cause much interference. Shipping tends to stick to recognised routes, so you would place your hydrophones away from heavily used shipping lanes.


How much time is there between an underwater earthquake and the moment a tsunami hits the coast? Tsunamis can be caused by underwater earthquakes, like the 2004 tsunami in the Indian Ocean. The precise value will likely depend on where the earthquake is relative to the coast. But what is the time range between the moment the earthquake is detected and the moment a tsunami hits the coast? Is it minutes? Hours?

As a general guideline, the time between the actual earthquake and the time the tsunami arrives is on the order of minutes to hours.
Tsunamis out in the deep ocean travel very fast -- around 500 mph -- which means that they cross ocean basins in about as much time as it takes a plane to fly across. So an earthquake in Japan gives Hawaii and the US West Coast many many hours of warning.
Close to shore, tsunamis are much slower, but of course the distances are also shorter. For a place where earthquakes happen close to shore because of a subduction zone, say in Japan, this only gives you a few or a few tens or minutes of advance warning. If you take the 2011 tsunami in Japan, warnings were sounded quite quickly, and that allowed a large number of people to get to higher ground and will have saved tens of thousands of lives. But at the same time, it was not enough time to actually go door to door, put people onto buses, and get them out of harm's way if either they did not hear the warning, or refused for whatever reason to immediately evacuate.


What happens to the higher frequency content of the tremors associated with a volcanic eruption? From [1]:

The main characteristics of volcanic tremor depend strongly on
whether a volcano is erupting explosively and on the intensity of the
event. Long before an eruption, tremor is ‘narrow-band’ (about
0.5–2 Hz and sometimes monochromatic or harmonic). On transitions
to active volcanism, however, the maximum frequency can climb
to 5–7 Hz (refs 1–4) (Fig. 1c and d). Moreover, whereas tremor related
to low-intensity volcanism remains narrow-band, tremor associated
with intermittent or protracted explosive behaviour is ‘broadband’,
characterized by power distributed over the full 0.5–7 Hz tremor
bandwidth.

When I read the above, I was not surprised that the tremors associated with volcanic activity were mainly comprised of low frequency components.  What did surprise me was that there isn't more high frequency energy present in the measurements.
Is there a noticiable amount of high frequency activity in these tremors that is not significant enough to report, or are the higher frequency components absorbed by the crust surrounding the volcano?

References:

Jellinek, A.M. and Bercovici, D. (2011).  Seismic tremors and magma wagging during
explosive volcanism.  Nature, 470, 522-526. [DOI]

It appears that, particularly for volcanos expelling andesitic magmas (those having a higher silica content, among other characteristics), the higher frequencies are more prevalent during the explosive portions of eruptions and tend to be absorbed by the more viscous magma during the tremors.

From the conclusion section of [1]:

  The principal distinction between the basaltic and andesitic cases can be identified in the difference of the magma-gas flux between the basic state and the explosive events. In other words, the high viscosity of the andesitic magmas reduces the permanent degassing, thus producing a low-energy volcanic signal (volcanic tremor) and inhibiting higher vibration modes, whereas the explosions are induced by strong
  emission of magmatic fluids, whose ascent along the conduit
  produces Vulcanian-style eruptions and the excitation of several vibration modes.

Some eruptions, e.g., the 1930 eruption of Stromboli are considered to be Vulcanian and would probably have slightly more energy in the higher frequency bands.

  Reference:

  De Lauro, E., De Martino, S., Falanga, M., Palo, M. (2011) Self-sustained vibrations in volcanic areas extracted by Independent Component Analysis: a review and new results. Nonlin. Processes Geophys., 18, 925–940  [DOI] [PDF] (freely available)


How to figure the MODIS snow cover against different elevation zones? I am working on MOD10C2 (8days snow cover), when I clip the MODIS snow cover against different zones of elevation the results was against general trend .i.e. the higher elevation was showing lower snow cover and lower elevation was showing higher snow cover. I did the with matlab inpolygon function, (inpolygon(xq,yq,xv,yv)) I am could not figure out where I am making mistakes.

First of all I imported the data in this way

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 = cell(num_files, 1);
Eight_Day_CMG_Clear_Index = cell(num_files, 1);
Eight_Day_CMG_Cloud_Obscured = cell(num_files, 1);
Snow_Spatial_QA = cell(num_files, 1);
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

Then reshaped data in this way

B2 = zeros(3600,7200,24);
for i = 1:3600
for j = 1:7200
B2(i,j,1:24) = reshape(Eight_Day_CMG_Snow_Cover{i,j},[1 3 2]);
end
end

Then generate lat lon and subset the area of interest

lon = -180:0.05:180;
lat = -90:0.05:90;
subsetqa = Eight_Day_CMG_Snow_Cover(2001:2817,4801:5741,:);

then attempted to extract with elevation polygon,
I tried both [monthly] and [8days] fractional snow cover data, elevation polygon are extracted from SRTM DEM. I attempted to use matlab in this way

shapefile = 'shapefile.shp' ;
S = shaperead(shapefile) ;

N = length(S) ;
for i = 1:N
    plot(S(i).X,S(i).Y)
    hold on
end
%%
lon = load('testlon.mat') ; lon = lon.testlon ;
lat = load('testlat.mat') ; lat = lat.testlat ;

[X,Y] = meshgrid(lon,lat) ;

data = load('testarray.mat') ; data = data.testarray ;
[nx,ny,d] = size(data) ;

%%Extract data
iwant = cell(d,N) ;
for i =1:d
    A = data(:,:,i) ;
    for j = 1:N
        idx = inpolygon(X(:),Y(:),S(i).X,S(i).Y) ;
        iwant{i,j} = A(idx) ;
    end

Then output iwant converted into matrix in this way

test = cell2mat(cellfun(@transpose,iwant,'uniform',0));

NOTE: this picture is showing polygon of only one elevation zone and we have six 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.


Is heavier bodies fall faster than lighter ones Suppose we have two stones, the first being lighter than the second. Release the two stones from a height to fall to Earth. Stone 2, being heavier than stone 1, falls more rapidly. If they are joined together, argues Galileo, then the combined object should fall at a speed somewhere between that of the light stone and that of the heavy stone since the light stone by falling more slowly will retard the speed of the heavier. But if we think of the two stones tied together as a single object, then Aristotle says it falls more rapidly than the heavy stone.

The question is how do the stones know if they are one object or two?

The two stones will accelerate at the same rate due to gravity. This is usually attributed to Galileo although it was known before that - eg. John Dee was aware of it and didn't think it a new revelation.

So if the two stones have the same shape and size - ie. so aerodynamic effects are identical, they will fall at the same acceleration and speed.
There are plenty of experiments to demonstrate this, but perhaps one of the most well known was by one of the Apollo astronauts (the Moon being essentially airless has no aerodynamic effects).  The result can also be derived from Newton's Inverse Square Law of Gravity and his Second Law of Motion.

If I were you, I would forget about Aristotle when it comes to trying to understand the world around us. He didn't have any concept of forces, inertia, and momentum as we would understand it today. His concept of forces was provably wrong with knowledge available to him at the time.


Are you more likely to get a sunburn at the pole than the equator? After looking at this post I can see that a flat object is more exposed to the sun at the equator. Does this mean that a person walking around (standing up), is much more likely to burn at the poles than at the equator? Because their entire body is facing the sun, not just the top of their head?

Assuming that your skin is exposed, you are standing upright are out when the sun is out, yes.

You don't even need to be very polar.

I live near Edmonton Alberta -- latitude 54 degrees.

At the equinox, the sun is at 54 degrees from the zenith.  At winter solstice it is 54 + 23 = 77 degrees from the zenith, or 13 degrees off the horizon.

No joke:  We use our car's sun-visors driving south at noon.

So if I'm standing up outside, I'm getting roughly the kind of sun intensity you would get lying flat on the beach in more tropical climes.  Since you aren't really hot, you can get a burn without your usual warning signs.

Confounding factors:

The sunlight is traveling through more atmosphere which reduces intensity somewhat.  At 13 degrees, the light path has about 4 times as much air in the path.  Which increases the amount of sunlight that is absorbed -- but not by a factor of 4.  The difference between sunlight in space and sunlight at the surface is about 30% overall, but adding to the air column will only increase the absorption of frequencies that are partially absorbed by a more vertical column.

Polar latitudes, and less polar ones in winter when solar angles are low, will tend to have less dust, water and smoke in the air, increasing air's transparency.

You get reflection off the snow which can come close to doubling your exposure.  Some of the worst sunburn's I've gotten have been on winter days on my poor nose.  (Sticking out of my face it's getting direct sun, reflected off of snow, and reflected off of face.)

One useful measure of the relative intensity is to look at the exposure information for photographs.  (You have to know the film speed or digital ISO as well as shutter speed and f/stop) I find that exposures in sunlight at 'low noon' in winter are comparable to summer ones.

Day lengths are short.  At winter solstice we theoretically have about 9 hours of daylight.  When the sun peaks at 13 degrees however there are a lot of shadows.

The weather tends to discourage large displays of skin, although I've split wood without a shirt on a calm sunny day at -10 C.  In general when dressing for outdoors I figure that bright sun in winter is equivalent to 10-15C degrees warmer, since it's hitting more of me.


How often do king tides (perigean spring tides) occur? Recently, there has been a lot of information about "King Tides" (i.e, perigean spring tides) on the news with a number of groups relating the peak king tide to future normal conditions (2050) under sea level rise scenarios along the east coast of the United States.

The effects on the magnitude of the tides are obvious when we compare normal tides (OSU model results for a day in 2010)

with the recent tides for the entire planet (spring tides):
.

While most of the difference is associated with the spring tides, about 2-3% is associated with the relative position of the Moon.

My question is related to the recurrence interval of perigean spring times. Some sources mention that they occur 3-4 times a year. If that is the case, then why such a focus on the mid-October 2016 event?

Another related question, which constituent of the tide is associated with the perigean/apogean oscillations?

The perigean spring tides arise when the spring tide occurs during the time of shortest distance between Moon and Earth. As the full moon on October 15-16, 2016 occurred near perigee, then it can be consider a "supermoon" even if the difference in Moon size was not that great. The fact is that the October full moon was not even the largest of the year; that will occur in November:

  The November 14, 2016 full moon – closest and largest full moon is 2016 – will also be the largest full moon thus far in the 21st century (2001 to 2100). It’ll be the closest encounter between the Earth and moon until November 25, 2034.

Thus, the mid-October was exceptional in a sense, but will be surpassed by the  November event.

With regard to the effect on tides, the combination of the $M_2$ and $S_2$ tidal constituents gives rise to the spring/neap cycle. Meanwhile, the $N_2$ tidal constituent arises from the fluctuating distance of the Moon from Earth and its frequency is 1 cycle/month less than that of $M_2$ and 3 cycles/month less than that of $S_2$.

We can resynthesize a time series of tidal elevation  based on only these three tidal constituents ($M_2$, $S_2$, and $N_2$). The resulting time series of amplitudes has an envelop that shows the size of the spring tides. As can be seen in the figure, even including only these three constituents, the resulting variability in the size of the spring tides (12 years shown) is pretty high. The problem is how to define "king tides". There are some spring tides that are larger than others, but even among the largest there is variability. This is where the uncertainty on the definition of the recurrence interval of the "king tides" comes from. Ultimately, it seems that having ~2 peaks ("king tides") per year is a good approximation. Including 3 constituents is a significant simplification and the entire series gets more complicated when additional constituents are included and defining a "king tide" is also more difficult.


Could pumping ocean water into desert areas help reduce sea level rise? I have been reading the discussion about pumping sea water into the desert for purposes of irrigation and changing the dry climate of that area, but what about to just purely offset the rising sea levels? Could pumping ocean water (with solar power) north of Malibu California into the dry areas help with coastline erosion and put water into the desert as a secondary but not primary benefit?
I am a completely non-scientific person who just happens to have an interest in coastal erosion so I welcome all your educated thoughts on this. Ideally it would be done in all the deserts globally to have a coordinated effort. Think of the infrastructure jobs in places like Africa. It would require a “world bank” initiative to create the funds to do this but as a global coordinated effort it may be required as part of planet rehabilitation. Yes I said “create” the funds, ie “print money” which would have a slightly inflationary effect on world economies but would mostly “inflate” depressed economies through wealth redistribution since these economies would likely be the major supply of labor. Like we see in Canada when the people from poor fishing towns went to work on the pipelines and brought cash back home.

This is no an answer per se, just a back-of-the-envelope calculation for fun.

Lifting 1 kg (one litre) of water up a height of 1 meter uses 9.8 (let's say 10) joules of energy.

Let's say you want to lower the sea level by 1 meter. You need to pump 3.6e17 litres (3.6e14 m2 of ocean area = 3.6e14 m3 to pump * 1000 for litre conversion).

Let's say you want to lift this water up a height of at least 10 meters, so it doesn't go right back into the ocean but rather stays behind some kind of dike. We're already at 3.6e18 J of energy. That's 1e15 Wh, or 1000 TWh. That's more than what the 20 largest power stations combined produce in a year.

This also assumes an ideal, 100 % efficient pump (no energy loss)...


Is there evidence to delineate where a southern extension of the East African Rift 'plate boundary' is developing? The East African Rift system is described by James Wood and Alex Guth of Michigan Technological University in the Geology.com web-article East Africa's Great Rift Valley: A Complex Rift System as being a term grouping the Ethiopian Rift and both the Western and Eastern branches of the East African Rift.  As shown in the image from the Tulane University web page Volcanic Landforms:

The soon-to-be-plates either side of the rift have been named the Nubian (west) and Somali (east) by many authors (as per the Wood and Guth article linked above).

The southern extent seems to lead offshore near Madagascar, which leads me to the question, what evidence is there to delineate where a southern extension of the East African Rift 'plate boundary' is developing?

There is considerable evidence that as the East African rift system is still developing, that it is not been possible to delineate southern rifting structures, which would extend into the Indian Ocean south of Madagascar. Looking at the evidence found in recent studies confirm this:

First to set the overall tectonic scene by Saemundsson (1), the East African Rift is a series of parallel rifts, particularly the east and west rift systems.  Rifting is propagating southwards, with rifting rates decreasing from 2.6 cm/year in the Red Sea area to less than 0.5 mm/year south of Kenya/Tanzania.

The entire rifting region is seismically active, with seismicity extending south into Mozambique, as can be seen in the earthquake map below (2):

There are no recorded earthquakes extending from the African continent to the plate boundaries to the south or east.

Peter Bird, of UCLA, published observations of the stress and strain features of southern Africa, particularly as the southern extension of the East African Rift provides a unique opportunity to observe new plate boundary formation.  Observational evidence provided by Bird et al (3) have found that there may be some very slow rotation of the developing 'Somali Plate' (East African plate) with respect to the 'Nubian plate' (rest of Africa) which is meeting resistance through an unbroken lithosphere - which may also partly explain the different spreading rate in the rift itself from north to south (1) (mentioned above).

The stress direction model developed by Bird et al. is shown below (3), what can be see is that the normal faulting from the East African Rift continues someway off the coast of Mozambique, becoming transversal (strike-slip), leading to a compressive feature (thrust faulting) near the plate boundary.

More studies would be required in the Indian Ocean off the coast of Mozambique would be required to definitively determine the extent and dynamics of this possible southern extent of the East African rift.

References

(1) Saemundsson, 2010, East African Rift: An Overview, Presented at Short Course V on Exploration for Geothermal Resources

(2) Midzi et al. Seismic Hazard Assessment in Eastern and Southern Africa

(3) Bird et al. 2006, Patterns of stress and strain rate in southern Africa, Journal of Geophysical Research


Coal fires affecting seismic activity, and also volcanoes Can coal fires cause earthquakes to happen, or possibly prevent or postpone earthquakes? Also can coal fires trigger(or prevent) volcanic eruptions by affecting flow of magma underground or by some other means?

I'm assuming you mean underground fires within coal seams whether they are caused naturally or through human neglect or error.

Regarding the possibility of them "triggering volcanic eruptions by affecting the flow of magma or by some other means", the answer is no.

Underground coal seam fires are mostly smoldering type fires rather than raging fires, due to the lack of oxygen underground. Most are located far away from sources of magma: either laterally or in terms of depth.

Concerning whether coal seam fires can "cause, ... prevent or postpone earthquakes", they cannot prevent or postpone earthquakes. Can they cause a seismic earthquake? Unlikely.

Coal seam fires create a void underground. If the fire is extensive and the extensive burnt out zone abuts an active geological fault the resultant void may cause a ground stress redistribution which might cause a minor earthquake. The other possibility is that the geological strata overlying the burnt out void is weak and eventually subsides, either slowing or quickly. A quick subsidence can create a minor earth tremor and could cause a hole to puncture the surface of the earth, depending on the depth of the coal seam and the void.


Are clouds needed for lightning? In big clouds of water vapor their could raise a difference in charges high and low in the clouds but is it necessary that there are clouds? Could there be lightning just with airstreams which create lightning?

Sometimes they say that life began in a mixture of liquids like methane hydrogen and ammonia together with lightning. But in the beginning of the earthlife were there already clouds to form lightning?

As far as I know; no. Lightning can either come from large amounts of friction in the atmosphere, or from a discharge of built up energy sourced from space (primarily solar radiation) captured by Earth's magnetosphere. The same energy that causes the Auroras.

That said, clouds will probably be present regardless of the source of the energy.


What does the term "topology" mean in geology? What does the term "topology" mean in geological sciences? Is there an agreed upon distinction between the two terms "topography" and "topology"?
When trying to answer this question, I came across the article "On the topology of topography: a review" by Keith Clarke and Boleslo E. Romero, however from reading the abstract of this article it seems like they use the mathematical definition of topology. On the other hand, in this answer, the term "topology" seems to be used as a synonym to topography.

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.


Ship turning causing calm water Can anyone tell me the official or technical term for the effect of when extremely calm water appears when a ship turns sharply in a full circle? I realise it could be related to wakes, but is not a wake in the traditional sense.

For example:

Has any research or modelling been conducted on this effect?

Thanks

Richard

I feel bad answering this one without any research reference, but I can tell you that what you are seeing in the OP's picture is a 'man overboard drill.' In that drill, the objective is to rotate the ship's rear section away from a man who many have fallen overboard and then circle the ship around (in an exact circle) to come up alongside the fallen man.

In order to do this with a ship, you throw the rudder over hard and fire up the engines ahead full. With many warships, the engine's toque is enough to produce massive cavitation (bubble formation) for the duration of the turn. As you end the turn you will usually throw a 2/3 backing bell, which will produce even more cavitation. That is the 'trail' you see behind the ship.
Specifically, the ship you see there is HMS Echo, which has a diesel-electric azimuth drive mounted below the ship, which will provide a very high torque.

Lots of cavitation (and you can feel a 10,000 ton ship vibrate during these maneuvers) means lots of bubbles are formed up to 15m below the surface (depending on the size of the ship; only about 6m for the pictured Echo). As the bubble propagate to the surface they disrupt the passage of surface wavelets and form the calm area shown.

One more factor is that navies train to put the man on the leeward side of the ship during recovery. Thus, in this picture the stern was swung to port (as we can see from the 'trail') so the man must have been on starboard, so the man is on the 'starboard' side of the ship during this recovery. That means the wind should be on the port side, so the calm area is being protected from the wind by the mass of the ship.