Why are titanium oxides lumped in with magnetite for the purposes of analyzing water bubble nucleation? I was reading a bit about the fluid mechanics of volcanoes to further understand some of the dynamics of "magma wagging" from my earlier question.
From [2]:

Heterogeneous nucleation, in which the presence of certain crystals (e.g., Fe-Ti oxides) provides nucleation substrates because they lower surface tension, can greatly
reduce $\Delta p_{s_{crit}}$ to less than a few megaPascals (Gardner & Denis 2004, Hurwitz & Navon 1994).

I became curious as to what was so special about the Fe-Ti oxides that makes them good nucleation sites, so I looked up [1].  I stumbled upon a different problem, though.  In their procedure, they claimed:

Fe-Ti oxides are either blocky or needle-shaped. Blocky ones
are 0.5–5$\mu$m in size and those large enough were identified
with an electron microprobe as titano-magnetite, here after
referred to as magnetite.

Do mixed Fe-Ti oxides essentially have the same physical properties as magnetite?  If the physical properties are in fact even slightly different, why would they be lumped together for analysis in these nucleation studies?

References:

Gardner, J.E. and Denis, M-H. (2004) Heterogeneous bubble nucleation on Fe-Ti oxide crystals in high-silica rhyolitic melts. Geochimica et Cosmochimica Acta, 68:7, 3587–3597. [DOI]

Gonnermann, H.M. and Manga, M. (2007). The fluid mechanics inside a volcano. Annu. Rev. Fluid Mech, 39, 321–56. [PDF] [DOI].

In iron oxides, titanium commonly substitutes for iron in the crystal structure. Two solid solution series are important: the ulvöspinel-magnetite and ilmenite-hematite series.

The composition of a solution changes continuously, without any change in crystal habit. Your reference indicates its the size of crystals than matter for being efficient nucleation sites. Composition is not expected to be important.


How to measure stretching direction on chocolate-tablet boudins? This has always bothered me during field mapping: A boudin with two stretching directions has the form of an M&M candy (or almond shape). What do I do to measure the two stretching directions if the surrounding rocks don't show any stretching lineations. Does anybody have experience with this?

If you can't get a macroscopic sense of lineation, try microscopic measurement  (thin sections along the supposed plane principal stresses) and transfer them in macro. The Techniques of Modern Structural Geology: Strain Analyses Vol I would be very useful for reference.


What is the mechanism behind changes in oxidation state (of iron, for example) as magma rises? From this answer, I learned that rising magma may change oxidation state, which I wasn't aware of.

In [#1], the authors state in the abstract

  The conventional view holds that the oxidation state of a mantle-derived degassed magma reflects its source

From other research that has been conducted, the authors discussed that the oxygen fugacity (the partial pressure of the "non-ideal" oxygen in the presence of the high pressures and temperatures, presumably of the magma itself) was controlled by a pair of redox reactions (their equations 11 and 12):

$$\ce{S_{(melt)}^2- + Fe2^3+O3_{(melt)} <=> SO2_{(gas)} + 6Fe^2+O_{(melt)} + O_{(melt)}^2-}$$
$$\ce{SO4_{(melt)}^2- + 2Fe^2+O_{(melt)} <=> SO2_{(gas)} + Fe2^3+O3_{(melt)} + O_{(melt)}^2-}$$

Their claim is that the change in the $\ce{Fe^3+}$ to $\ce{Fe^2+}$ ratio is due to the top reaction being favored during decompression (due to the equilibrium shift from the changes in pressure).

I get all of that so far, I think.  My question is a more fundamental one, being, if this equilibrium (#11) governing the changes in oxidation states becomes more prevalent as the pressure decreases rapidly, what are the governing equations for when the magma is deeper in the earth?  Do they favor the bottom equation (#12) instead, or are the reactions at that temperature and pressure completely different?

It's possible I'm missing what the "changing oxidation states" aspect really means. A clarification of that would certainly be appreciated.

  Reference:

  Moussallam, Y., Oppenheimer, C., Scaillet, B., Gaillard, F., Kyle, P., Peters, N., & Donovan, A. (2014). Tracking the changing oxidation state of Erebus magmas, from mantle to surface, driven by magma ascent and degassing. Earth and Planetary Science Letters, 393, 200-209. [DOI]

My question is a more fundamental one, being, if this equilibrium (#11) governing the changes in oxidation states becomes more prevalent as the pressure decreases rapidly, what are the governing equations for when the magma is deeper in the earth?

My interpretation is that at the volcano being studied, the right side of equation 11 is favored at low pressure, and the left side is favored at high pressure (greater depth).  Deep magma there contains sulfide ($\ce{S^2-}$) in accordance with equation 11, rather than sulfate ($\ce{SO4^2-}$) which would involve equation 12.  So equation 11 applies to all depths being studied and equation 12 does not apply to this volcano at any depth.


Science behind the groundhog day myth The myth goes that if a groundhog sees his shadow on Feb 2, six more weeks of winter are yet to come, but if he doesn't, spring is just around the corner.

I am wondering if there is any science behind this myth, or perhaps an origin based on observations of actual weather patterns relating to the seasons.

Never mind the groundhog, I am thinking about sky conditions and the seasons.

Specifically, where I am in southern Ontario Canada, winter is usually at its coldest around the start of February. At this time of year we are often under an arctic high pressure "dome"; skies are often clear. As the cold gives way to milder weather, we often end up under the jetstream, or at least it sweeps back and forth over us, delivering a lot of stormy, wintry weather, after which the jetstream at the boundary of the polar air mass eventually retreats north and spring arrives.

Is this an oversimplification? If not, could this sort of pattern be the origin of the myth: generally clear skies mean winter remains settled in, more unsettled weather means winter is about to give way to spring?

It is a myth. To say that it is unscientific is perhaps not the best phrasing, but Groundhog day isn't the most accurate prognostication method around. For something to be scientific, it needs to follow the scientific method. Given that Groundhog day follows the myth that:

A groundhog that sees it's shadow will become frightened, seeking shelter from the upcoming chill of 6 more weeks of winter, or
A groundhog that doesn't see it's shadow is certain that a chill is not on its way

, the implication derived from a poem being that nice weather now means bad weather later and vice-versa. Without defining 'winter' or 'spring' I question the testability of the hypothesis. In reality, astronomical spring occurs 6 weeks and 4 days after Groundhog day, making the distinction between the groundhog's "definition" of spring entirely subjective. But translating a subjective metric into an objective criteria, it is less accurate than the Farmer's Almanac of 50%.
If you want a more likely explanation on why a groundhog might behave the way it does, one need not look much farther than a picture from Punxsutawney, PA on groundhog day:

Given such a crowd, will an animal react more about the crowd or the upcoming weather? My guess is that if you saw such a crazed crowd show up to your house at 7:25 AM, you would want to crawl into a hole too. A groundhog must go into it's hole at some point, and I don't know of a part of the myth that says that the groundhog must immediately go back into its hole. Doing a groundhog day tradition with so much festivity, scientifically (not necessarily rationally though), be a contaminating factor to any sort of experiment. That is, even slight deviations in how such a thing is conducted (and given that groundhogs like Phil basically live like royalty in comparison to groundhogs in their natural state) make any rigorous evaluation of Phil's prognostication ability questionable.


What is the potential impact of hurricane intensification and sea level rise on coastal flooding? Climate change has the potential to increase flooding by hurricane intensification (magnitude and intensity) and by sea level rise. Many communities around the world are already exposed to coastal flooding from tropical cyclones. How much will the changes associated with climate change enhanced coastal flooding? I know this is largely region specific, but I would like to know of studies that quantify these effects.

As you noted, the (predictions about) impacts of global climate change are region specific. The general reasoning behind predictions for many regions are similar however, as are some noteworthy bottom lines (such as increased exposure to flooding as your question focuses on), so I'll try to summarize those from rich and reliable sources.

The Maryland Sea Grant website states on its Storm Surges page:

  As sea level rises, flooding due to storms will worsen so that homes and buildings already vulnerable to storm surges will sustain more damage. Powerful storms can damage dwellings or flood low-lying areas with high tides and wind-driven waves. Some Maryland neighborhoods could be underwater completely.

  ...

  A special report, Come High Water: Sea Level Rise and Chesapeake Bay, offers a comprehensive look at the causes and consequences of increasing flooding along Maryland’s coasts. This package, produced by Maryland Sea Grant's magazine Chesapeake Quarterly in partnership with Bay Journal, examines the scientific understanding and projections of the rate of sea level rise in the Chesapeake region; effects on people and the environment; and adaptations and policy responses that are under way or under consideration. An online interactive page offers videos, photos, and an interactive map showing flooding that is predicted to occur.

The PDF of "Come High Water: Sea Level Rise and Chesapeake Bay" has more detailed explanations, data, and citations, and is available from that page.

On its Rising Sea Level page:

  Scientists have forecasted an increase of as much as 2.1 feet in the Chesapeake Bay by 2050. And by the end of this century, that number could be 3.7 feet or higher. Scientists have identified several reasons, which are described in a video produced by Maryland Sea Grant (below):

  As oceans around the world grow hotter, they also expand because of the chemistry of salt water.
  Glaciers and ice caps, including the icy regions around Greenland, are melting.
  Since the end of the last ice age, the land around Maryland has been naturally sinking a tiny bit each year — a trend that has accelerated over recent decades.

That article linked to where the above quote says "Scientists have forecasted..." has a worthwhile introduction to answer your question and provide context to the source:

  State Should Consider Longevity of Structures Built Near Water, Report Says

  Maryland residents should plan for the state’s coastal waters to rise by 2.1 feet by the year 2050 and by 3.7 feet or more by the century’s end, according to a new analysis by scientific experts prepared for Gov. Martin O’Malley. The panel reviewed recent evidence about sea level rise in Maryland and globally.

Based on your asking about specific studies that quantify these [climate change] effects: if you are interested in more detailed data and prediction methods, you can easily find various climate forecasting models if you search for them. One example is NARCCAP. It and many other climate forecasting models are public-domain but fairly technical. Another example is https://www.data.gov/climate/coastalflooding/

Many climate change impacts are predicted based on environmental models and the data sets they produce. For a glimpse at modeling efforts very relevant to your question, you could explore these papers:

DesAutels, 2000. Upper-Ocean Influences on Hurricane Intensification
Modeling.
Hellegatte et al, 2010. Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen.
Mousavi et al, 2009. Global warming and hurricanes: the potential impact of hurricane intensification and sea level rise on coastal flooding.
NCAR Article: Evaluating the Effects of Future Sea Level Rise and Storm Surges Along U.S. Coastlines and associated paper: Tebaldi et al, 2012. Modeling sea level rise impacts on storm surges along US coasts. Both of these support and address your question in the first place.

The Hellegatte et al, 2010 paper is focused on economic analysis which you may be interested in:

  For  instance,  the  total  losses  (direct  and  indirect)  caused
  by  a  present-day  100-year  storm  surge  event,  at  150  cm  above  normal  sea  level,
  are estimated to reach EUR 3 billion with no protection. In the aftermath of such
  an event, thousands of jobs would be lost and thousands would be created in the
  construction  sector.  Other  types  of  impact  have  been  disregarded  here,  but  may
  also be important (e.g., casualties, illness, psychological trauma, disruption of social
  networks, loss of national competitive strength and market positions, loss of cultural
  heritage, city attractiveness, etc.). Even with the relatively narrow set of economic
  impacts  considered  here,  the  reconstruction  process  would  last  several  years  and
  cause a significant shock to the local and the national economy.

The Mousavi et al, 2009 paper provides specific predicted sea level rise quantities which may be more what you are after:

  For  the  Corpus  Christi,  Texas,
  United  States  study  region,  mean  projections  indicate  hurricane  flood  elevation
  (meteorologically  generated  storm  surge  plus  sea  level  rise)  will,  on  average,  rise
  by 0.3 m by the 2030s and by 0.8 m by the 2080s. For catastrophic-type hurricane
  surge events, flood elevations are projected to rise by as much as 0.5 m and 1.8 m by the 2030s and 2080s, respectively.

Again there are many modeling/prediction and analysis efforts and varying scenarios for different regions. If you were more specific about the region and particular analysis you're interested in, folks could probably share more specific answers.

TL;DR: Sea level is predicted to rise due to climate change, to varying extents place-to-place, and that rise in sea level will increase the magnitude of damage many places are exposed to due to storms, which are also predicted to pose a more significant threat due to increased intensity and frequency.


Current contaminant levels from Deep Water Horizon oil spill? The Deep Water Horizon oil spill occurred in 2010 and is known as the largest marine oil spill ever. I heard on the radio the other day, from a not so reliable source, that contaminant levels have dropped in the 90% range since then due to naturally occurring organisms that feed off the hydrocarbons.  Is there any good current research out there now that shows what current concentrations are compared to what they were after the spill?

Concerning

Is there any good current research out there

the most recent study I found is

Persistence and biodegradation of oil at the ocean floor following Deepwater Horizon

In this work, we assess the extent of degradation over 4 y postspill
for each of 125 petroleum hydrocarbons that contaminated the seafloor.

and compares levels from thousands of sample sites. This seems to qualify 'good current research'.


How do I start with Modular Ocean Model version 6 i.e. MOM6 on my ubuntu linux machine? I am learning ocean modelling and have started with MOM6. I needed guidance on where to start from and how to run the model on my local machine (Ubuntu) to perform various tasks such as regridding or creating initial conditions or maybe small runs which might not take much time.

You can follow the instructions I wrote at https://medium.com/@manmeet20singh11/installing-mom6-on-ubuntu-57918a200293?sk%3D9961d6f8565659b05e007349c47568b7&sa=D&source=hangouts&ust=1576058141492000&usg=AFQjCNH74gi6j9XgT6I3509nb8Y0gtj4-Q

Installing MOM6 on Ubuntu

  Downloading the model and basic libraries

Following https://github.com/NOAA-GFDL/MOM6-examples/wiki/Installing-MOM6-in-a-virtual-machine-running-Ubuntu

And the video https://www.youtube.com/watch?v=a5hKh8s_1b0&t=181s

sudo apt-get install git tcsh pkg-config
sudo apt-get install gfortran
sudo apt-get install netcdf-bin libnetcdf-dev libnetcdff-dev
sudo apt-get install openmpi-bin libopenmpi-dev
sudo apt-get install libnetcdff-dev
git clone --recursive https://github.com/NOAA-GFDL/MOM6-examples demo
cd demo

Open the file src/mkmf/templates/linux-gnu.mk and find the variable LIBS. Now in this file add the syntax

# Add -lnetcdff
LIBS += -lnetcdff

Compiling

Now we are ready to start compiling and that we will do by going to https://github.com/NOAA-GFDL/MOM6-examples/wiki/Getting-started#compiling-the-models

The instructions are for setting up on an intel compiler so we have to do some editing on these commands because we are going to use gfortran which is GNUs fortran.

Make a directory to work to build the advanced libraries

mkdir -p build/gnu/shared/repro/
cd  build/gnu/shared/repro/

Next, we will run the mkmf list path tools and we are going to give it the relative path to FMS the source code

../../../../src/mkmf/bin/list_paths -l ../../../../src/FMS

So what you have done is populated this file path_names with all these files and now we are going to run another mkmf tool and care of itself and we are going to specify the linux gnu template that we edited above and we are going to tell it to build FMS

../../../../src/mkmf/bin/mkmf -t ../../../../src/mkmf/templates/linux-gnu.mk -p libfms.a -c "-Duse_libMPI -Duse_netCDF -DSPMD" path_names

and that makes the Makefile

Finally we can type

make NETCDF=3 REPRO=1 FC=mpif77 CC=mpicc  libfms.a -j

So that builds the lib FMS code which can be seen by

$ ls -l libfms.a
-rw-rw-r-- 1 manmeet manmeet 13863094 Dec 10 13:22 libfms.a

Now we are going to go back and we will make a new set of folders ocean_only/repro

cd ../../
mkdir -p ocean_only/repro
cd ocean_only/repro

Now we are going to run the list paths command again and that is just down and this time we are going to provide the current directory as a possible supply of sorts and gauge you want to put some source files in here and thats the dot for, and then there is the relative path of source code for MOM6 and we are going to be linking a dynamic executable, so we are putting the dynamic directory out of the config source and we are building a set up using the solo drivers, the driver had a config source, and all of the source code under src is being used.

../../../../src/mkmf/bin/list_paths ./ ../../../../src/MOM6/{config_src/dynamic,config_src/solo_driver,src/{*,*/*}}/

So this has made a path_names file again which is populated will all the MOM6 source code. Now we are going to run the mkmf tool again and this time the target there is MOM6 and we have to change the template again to the linux-gnu

../../../../src/mkmf/bin/mkmf -t ../../../../src/mkmf/templates/linux-gnu.mk -o '-I../../shared/repro' -p MOM6 -l '-L../../shared/repro -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names

This will create the Makefile and now we can run the Makefile using a similar command but the target will be MOM6

make NETCDF=3 REPRO=1 FC=mpif77 CC=mpicc LD=mpif77 MOM6 -j

Now we have the executable just called MOM6 and we will demonstrate that it works.

Going to the top of the directory and then to double_gyre

cd ../../../../
cd ocean_only/double_gyre/
mpirun -n 1 ../../build/gnu/ocean_only/repro/MOM6

This is a simple 10 day test and will give something like this

$ mpirun -n 1 ../../build/gnu/ocean_only/repro/MOM6
NOTE: MPP_DOMAINS_SET_STACK_SIZE: stack size set to    32768.
&MPP_IO_NML
 HEADER_BUFFER_VAL=      16384,
 GLOBAL_FIELD_ON_ROOT_PE=T,
 IO_CLOCKS_ON=F,
 SHUFFLE=          0,
 DEFLATE_LEVEL=         -1,
 CF_COMPLIANCE=F,
 /
NOTE: MPP_IO_SET_STACK_SIZE: stack size set to     131072.
NOTE: ======== Model being driven by MOM_driver ========
NOTE: callTree: o Program MOM_main, MOM_driver.F90
NOTE: open_param_file: MOM_input has been opened successfully.
NOTE: open_param_file: MOM_override has been opened successfully.
 MOM domain decomposition
whalo =    4, ehalo =    4, shalo =    4, nhalo =    4
  X-AXIS =   44
  Y-AXIS =   40
 MOMc domain decomposition
whalo =    2, ehalo =    2, shalo =    2, nhalo =    2
  X-AXIS =   22
  Y-AXIS =   20
NOTE: diag_manager_mod::diag_manager_init: prepend_date only supported when diag_manager_init is called with time_init present.
 MOM domain decomposition
whalo =    4, ehalo =    4, shalo =    4, nhalo =    4
  X-AXIS =   44
  Y-AXIS =   40
 MOM domain decomposition
whalo =    4, ehalo =    4, shalo =    4, nhalo =    4
  X-AXIS =   44
  Y-AXIS =   40WARNING: depth_list_setup: ./Depth_list.nc does not exist.  Creating a new file.WARNING: depth_list_setup: ./Depth_list.nc does not exist.  Creating a new file.MOM Day       0.000      0: En 1.423707E-13, MaxCFL  0.00000, Mass 5.288178268008E+18
    Total Energy: 4126F9E3C5B6FEAF  7.5288188616176497E+05
    Total Mass:   5.2881782680077681E+18, Change:   0.0000000000000000E+00 Error:  0.00000E+00 ( 0.0E+00)
MOM Day       1.000     72: En 4.580432E-06, MaxCFL  0.00023, Mass 5.288178268008E+18
    Total Energy: 42B607A80A83D4D0  2.4222139843540812E+13
    Total Mass:   5.2881782680077681E+18, Change:  -4.3787057037831573E+01 Error: -4.37871E+01 (-8.3E-18)
MOM Day       2.000    144: En 8.958206E-06, MaxCFL  0.00040, Mass 5.288178268008E+18
    Total Energy: 42C58AE533474153  4.7372590026370648E+13
    Total Mass:   5.2881782680077681E+18, Change:   4.0318344822758547E+00 Error:  4.03183E+00 ( 7.6E-19)
MOM Day       3.000    216: En 1.166708E-05, MaxCFL  0.00045, Mass 5.288178268008E+18
    Total Energy: 42CC0E8B5CE02C7F  6.1697586479192992E+13
    Total Mass:   5.2881782680077681E+18, Change:   3.7798219410031322E+01 Error:  3.77982E+01 ( 7.1E-18)
MOM Day       4.000    288: En 1.542296E-05, MaxCFL  0.00044, Mass 5.288178268008E+18
    Total Energy: 42D28B60D5891799  8.1559337444446391E+13
    Total Mass:   5.2881782680077681E+18, Change:  -5.3715335655123397E+01 Error: -5.37153E+01 (-1.0E-17)
MOM Day       5.000    360: En 1.826022E-05, MaxCFL  0.00053, Mass 5.288178268008E+18
    Total Energy: 42D5F4B97D5D13C3  9.6563312817231047E+13
    Total Mass:   5.2881782680077681E+18, Change:  -2.1099290705067006E+01 Error: -2.10993E+01 (-4.0E-18)
MOM Day       6.000    432: En 2.397431E-05, MaxCFL  0.00062, Mass 5.288178268008E+18
    Total Energy: 42DCD397A5E14CB5  1.2678043163166683E+14
    Total Mass:   5.2881782680077681E+18, Change:  -1.0757841703346941E+01 Error: -1.07578E+01 (-2.0E-18)
MOM Day       7.000    504: En 2.927962E-05, MaxCFL  0.00073, Mass 5.288178268008E+18
    Total Energy: 42E19A5109E607AC  1.5483585791186938E+14
    Total Mass:   5.2881782680077681E+18, Change:   4.6503600220410505E+01 Error:  4.65036E+01 ( 8.8E-18)
MOM Day       8.000    576: En 3.486441E-05, MaxCFL  0.00081, Mass 5.288178268008E+18
    Total Energy: 42E4F5D995BB3999  1.8436920009364478E+14
    Total Mass:   5.2881782680077681E+18, Change:   2.4524203108417453E+01 Error:  2.45242E+01 ( 4.6E-18)
MOM Day       9.000    648: En 4.195565E-05, MaxCFL  0.00090, Mass 5.288178268008E+18
    Total Energy: 42E9393C9CAC4D82  2.2186896584766006E+14
    Total Mass:   5.2881782680077681E+18, Change:  -2.0342624683786397E+01 Error: -2.03426E+01 (-3.8E-18)
MOM Day      10.000    720: En 4.604729E-05, MaxCFL  0.00100, Mass 5.288178268008E+18
    Total Energy: 42EBAEF71B9A7E17  2.4350627232664072E+14
    Total Mass:   5.2881782680077681E+18, Change:   2.2570880028229340E+01 Error:  2.25709E+01 ( 4.3E-18)FATAL: NETCDF ERROR: No such file or directory File=RESTART/MOM.res.ncFATAL: NETCDF ERROR: No such file or directory File=RESTART/MOM.res.nc--------------------------------------------------------------------------
MPI_ABORT was invoked on rank 0 in communicator MPI_COMM_WORLD
with errorcode 1.NOTE: invoking MPI_ABORT causes Open MPI to kill all MPI processes.
You may or may not see output from other processes, depending on
exactly when Open MPI kills them.
--------------------------------------------------------------------------
-------------------------------------------------------
Primary job  terminated normally, but 1 process returned
a non-zero exit code.. Per user-direction, the job has been aborted.
-------------------------------------------------------
--------------------------------------------------------------------------
mpirun detected that one or more processes exited with non-zero status, thus causing
the job to be terminated. The first process to do so was:Process name: [[19779,1],0]
  Exit code:    1
--------------------------------------------------------------------------

Thus we will get an error when we haven’t created a restart. We will do that now

mkdir -p RESTART
mpirun -n 1 ../../build/gnu/ocean_only/repro/MOM6

Next we will use two processors and we see that it runs faster now

mpirun -n 2../../build/gnu/ocean_only/repro/MOM6

https://github.com/NOAA-GFDL/MOM6-examples/wiki/Ubuntu-compiler-environment


What ozone depleting chemicals can salt produce? According to How Long Will Life Last On Earth, 250 million years ago Siberia underwent a large a prolonged period of volcanic activity. It has been speculated that large salt deposits that existed in Siberia, and still do, were baked by the volcanic activity and large amounts of ozone destroying chemicals were released into the atmosphere.

What ozone depleting chemicals can salt produce?

A study presented in the Science Daily article (based on a Science article), Study: Sea Salt Seasons Chemical Brew That Destroys Arctic Ozone (Purdue University, 2001) could serve as an analogue to salt induced ozone depletion.

Sea salt, in particular, compounds formed from the constituent chlorine, bromine (and fluorine) found in most salts have been identified as ozone depletors - researchers have found that the sea salt in the Arctic containing these ions cause a seasonal thinning in the Arctic.  With a large concentration of salt in the strata, as found in the region of the Siberian Traps, it is not inconceivable to assume that large quantities of chlorine and bromine were released in the atmosphere.

Quantitatively, according to the article Magnitude and consequences of volatile release from the Siberian Traps (Black et al. 2012), the actual amount released was in the order of approximately ~3400–8700 Gt chlorine, of which a good proportion was, according to the article, assimilated into the magma from melting of the evaporite, salt and sedimentary rocks.  Also postulated by Environmental mutagenesis during the end-Permian ecological crisis (Visscher et al. 2004) was that the size of the Siberian volcanics caused the assimilation of volumnous organics and halogen based deposits (including salts).

These hypotheses are supported by experiments reported in the article Contact metamorphism, halocarbons, and environmental
crises of the past (Svenson et al. 2009), where rock salt from Siberia was heated to 275C released ozone depleting halocarbons such as methyl chloride (CH3Cl) and methyl bromide (CH3Br).

In general, considering the most common salt is sodium chloride (NaCl), once dissolved, the chloride ion (Cl-) is very reactive, and forms several ozone depleting compounds (such as the halocarbon examples above), and chlorine itself can react to 'tear apart' ozone itself, as shown in the diagram below:

Image Source: NOAA

The example used in the image above are manmade CFC's (chlorofluorocarbons), however, it can be applied to any halocarbon or for chlorine itself from step 3 onwards.

In conclusion, any halogen (particularly F, Cl, Br) based salt are potential ozone depletors, due to the reactivity of the halogen ions.  Especially Cl due to how common NaCl salt is, the Cl (and other halogens) can react with other chemicals that may be present also providing ozone depleting chemicals.  So, given the amount of salt that the Siberian Traps that would have heated up, the chances of a fairly large effect on the ozone layer at the time are pretty good.


How much Sulfur Dioxide is emitted into the atmosphere from a mine fire? Since burning coal leads to a variety of air pollutants, presumably a coal mine fire would also lead to air pollutants. My question is whether there have been any published studies on the emission rate of SO$_2$ from a mine fire, such as the Centralia fire in Pennsylvania?

All coal deposits are different. Coal chemist is highly varied, and the amounts of sulfur and heavy metals with the coal seams varies with each deposit & within seams. This is due to the conditions prevalent when the coal seams were formed.

Consequently, the amount of sulfur dioxide emitted when coal is burned, whether in a power station or a mine fire depends on the amount of sulfur in the deposit the coal came from.

Without having an assay of the amount of sulfur in the coal or measuring how much sulfur dioxide is generated in a fire it is impossible to know how much sulfur dioxide will be produced.


Should one extract wavelet from seismic or well log for the generation of synthetic traces? Synthetics are generated by convolving reflectivity series with the known wavelet. I have seen most of the wavelet are extracted from seismic data itself. Is there any hard and fast rule, or can one extract wavelet from well log as well? If so which gives better result in terms of correlation? The wavelet extracted from seismic or well log data?

Seismic, but...

There are lots of ways of estimating wavelets. None of them rely on well logs alone, because they don't contain any information about the wavelet. Some methods are purely statistical, some use the seismic data, and some of them use seismic and well logs.

Background

I recommend reading what you can about wavelet extraction. Especially these (see links to many of these here):

Brown, A (2001). Calibrate yourself to your data! A vital first step in interpretation. Geophysical Prospecting 49, p 729–733.
Roden, R and H Sepulveda (1999). The significance of phase to the interpreter: practical guidelines for phase analysis. The Leading Edge 18, July 1999, p 774–777.
Simm, R and R White (2002), Tutorial: Phase, polarity and the interpreter's wavelet. First Break 20 (5), p 277–281.
White R, and R Simm (2003). Tutorial: Good practice in well ties. First Break 21 (10), p 75–83.

Workflow

My approach, for what it's worth, is as follows:

Establish that your seismic data are zero phase. See this wiki article and this tutorial.
Tie your wells with a purely statistical zero-phase wavelet until you know better. A Ricker is fine for most impulsive data. Don't bother with autocorrelation. Don't bother with mixed-phase. Once you've found the best one, don't change it from well to well.
Take your best well ties (maybe the best 10 or 20 percent), and check them.
Try extracting zero-phase wavelets from your best data, preferably near angle stack, and unfiltered (e.g. by fk, which processors love) — you don't want any spectral shaping nonsense. Only use the zone of interest, but at least about 1000 ms of data. Avoid the near surface.
Average all the wavelets you got and tie all your wells with this wavelet. The ties should improve in most cases.

Note: Step 4 usually involves estimating the wavelet from the seismic data, using the reflectivity from the wells. It's basically the reverse of making a synthetic: seismic / reflectivity = wavelet, where / denotes deconvolution.

Caveat interpretor

I would complete the workflow for each seismic survey (e.g. a 3D, or all lines in a 2D). Your goal is to use a single wavelet for each survey. This goal reflects the assumptions in this workflow:

The reflectivity is random.
The wavelet is zero-phase or minimum-phase.
The wavelet is stationary (invariant in space and time).

None of these assumptions hold (Ziolkowski, 1991; Why don’t we measure seismic signatures? Geophysics 56, 190–201; see also this thesis). This is what makes it fun! Your job is to cope with this without breaking the laws of physics or what we know about geology.


What might cause these "wave clouds" near the horizon? Could it be gravity waves? I found this image on a website - returned as a HTTP error 404 'not found'.

But the clouds look strangely repetitive. At first I thought the image had been "photoshopped", but as @gerrit pointed out in a comment, each is different.

The sky locally is clear and blue, but there are clouds above the water in the distance.

Is this a real, recognizable type of formation? (the six repeated long, low clouds above the horizon in the distance) I have seen plenty of gravity wave cloud patterns in the sky and in the internet, but always from above, or below, never edge-on in a clear blue sky.

Image found here: http://wetlabs.com/sites/default/files/images/overboard.jpg

Identification of the phenomenon based solely on the photograph is unfortunately inconclusive, although several reasonable possibilities exist. While a wavelike structure is apparent, exact identification of the phenomenon is difficult due to the lack of cloud condensate (which would better illustrate the local vertical air motions ultimately driving cloud development) and of corresponding local weather observations (which could suggest which phenomenon is most likely to occur). Three possibilities seem most likely:

Horizontal Convective Rolls - a mode of convection in which clouds (and corresponding upward vertical air motion) preferentially organize in approximately linear lines, with downward vertical motion between (image from linked source). These clouds are observed over both land and water surfaces, although formation over water is more likely in cold weather when the water is relatively warm compared to land. This mode is possible, though perhaps least likely, given that weak clouds are visible across the horizontal length of the photograph - well-organized horizontal rolls commonly have clear air between the lines of cloud.  (image source)

Gravity wave clouds - clouds that form in the upward motion regions of large-scale "waves" of vertical motion propagating through the atmosphere. Gravity waves are a common phenomenon occurring in many different atmospheric environments, with the resulting clouds varying greatly in appearance depending on the strength of the vertical motion and the amount of atmospheric moisture available for condensation. Given this, gravity wave clouds are a reasonable possibility based on the photograph. (image source)

Kelvin-Helmholtz wave clouds - An overturning motion that may develop at the interface between two atmospheric layers with substantially differing horizontal winds, with cloud condensation (assuming sufficient moisture) taking on an appearance similar to rolling ocean waves (image from linked source). This explanation also seems reasonable, given that air masses with distinct characteristics commonly occur in the lower atmospheric boundary layer and between ocean and land masses. Anecdotally, the clouds in the photograph look most visually similar to near-surface Kelvin-Helmholtz wave clouds I've seen, although again, the lack of actual cloud condensate makes this difficult to assess with any certainty.  (image source)


Where can I find wave height/period, current velocity and wind speed/direction data at the Qiangzhou strait in China? I'm looking for time series of wave height/ period, velocity currents and wind speed/direction specifically for the Qiangzhou strait in China.

Global data sets could also work.

You can retrieve that information from ERA5 reanalisys data. It cover the whole world from 1979 until almost the present. The resolution is 0.25°x0.25° and includes many fields that characterize ocean waves. here is a list of available fields for download in the "Ocean Waves" section:

And there is of course, winds data as well.


A simple formulation to calculate the sequence and rate of salt precipitation in a brine? Is there a simple formulation to estimate the rate of salt precipitation in a brine given its ion concentrations and the evaporation rate?

For example: if we know the concentrations of Cl−, Na+, Mg2+, SO42−, Ca2+, K+ of a sea-water brine at a known temperature T, what salt(s) and how much of each will precipitate for a given evaporation rate E (assuming time enough for equilibrium)?
Can you refer to a concise text-book or article?

I need something that goes beyond the simplistic saturation concentration concept (i.e., prescribed limit for diluted salt concentration). The effects of life in the solution can be dismissed.

Here is a link to a hydrochemical consultant, Dr. C.A.J. Appelo, and the public-domain software FREEQC.

It can be downloaded from here

This link leads to an example of salt precipitation rates when evaporating sea water.

The formulation used is detailed in a second manual


SWAN wave model: HSIG is strangely low I have a question about SWAN wave model, I tried to ask it at the SWAN mailing list, but with no luck, so I'm asking here.

First of all I have to admit I'm not a professional SWAN user. All the knowledge of SWAN I have I have received from the user manual and google. Neither am I an oceanologist, so I can't say I really understand what the model does. However, I need to use it.

I used GridBuilder to generate the grid, that is why I'm using curvilinear grids (the grid is actually a rectnagle, but GridBuilder generates only curvilinear grids when exporting for SWAN).

I'm trying to compute wave properties using this task:

PROJECT 'p1' 'r1'
SET NAUTICAL
MODE DYN TWODimensional
PROP BSBT

CGRID CURVILINEAR 90 86 EXC -9999 CIRCLE 10 0.03 0.55 40
READGRID COORDINATES 1 'data.grd' 4 0 0 FREE
INPGRID BOTTOM CURVILINEAR 0 0 90 86 EXC -9999
READINP BOTTOM 1 'data.bot' 4 0 FREE

GEN2
WIND vel=20 dir=270
BLOCK 'COMPGRID' NOHEADER 'results.mat' LAY 3 XP YP BOTLEV WIND HSIG HSWELL VEL WATLEV DEPTH PER WLEN OUT 20200101.000000 5 MIN

COMPUTE NONSTAT 20200101.000000 10 SEC 20200101.003000
STOP

If I understand the syntax correctly, then SWAN should compute wave propagation during 2.30 hours using wind with speed 20 m/s that blows from the West. However, when I check the results in MATLAB, I see that HSIG is about 0.0012 or something like that. This looks strange to me, since I'd expect waves to be much higher when the wind speed is 20 m/s which is a gale wind.

I have computed the same properties for a whole day and the results are almost equal. These are the lines of the second task that differ from the first one:

BLOCK 'COMPGRID' NOHEADER 'results.mat' LAY 3 XP YP BOTLEV WIND HSIG HSWELL WATLEV VEL DEPTH PER WLEN OUT 20200101.000000 30 MIN

COMPUTE NONSTAT 20200101.000000 10 MIN 20200102.000000

However, period is about 2.0126634 and wave length is 5.0730591. This parameter combination looks strange to me.

Did I miss something while composing the task? Thank you for help in advance.

data.grd:
https://pastebin.com/RnsRssiY
data.bot:
https://pastebin.com/zbg4eySL

You need to use the following line in your model setup:

COORDINATES SPHERICAL

Otherwise SWAN will try to calculate wave properties on a curvilinear grid with coordinates provided in meters. In your present case, the computational area is 0.47 m x 0.24 m instead of degrees longitude and latitude.


Why is Sacramento so warm for its latitude? http://en.wikipedia.org/wiki/Sacramento,_California

It has high winter temperatures and high summer temperatures too.

The main reason for Sacramento's warmth compared to some other cities at the same latitude is, like in many cases, due to the city's surrounding topography, as can be seen in the topographic map below:

Source: Wikipedia: Geography of California

In regards to the climate of Sacramento, it is probably better to consider why it is seasonally warmer than cities on the same latitude.

Sacramento is located in a regionally low elevation Great Valley (also Central Valley) (1), in between two roughly parallel mountains ranges that converge in the north of the Valley in the Klamath Ranges. The Coastal Range on the west and the Sierra Nevada to the east (2).

The Köppen climate classification for Sacramento is Csa, which is defined as a Mediterranean environment with hot summers (1). The Coastal Range largely shields the city (and the Valley) from the climate (especially much of the humidity) of the Maritime climate to the west, and the Continental climate to the east (2).  Essentially, according to the WRCC, the climate of the Valley (including Sacramento) is intermediate between the climates either side of the bordering ranges (2).

The very cold winters that can occur in the Continental climate are blocked by the Sierra Nevada.  Even if cold air does get over the mountain, the air warms as it compresses as descends into the Valley, and hence is milder when it reaches the Valley (2).

In regards to the Urban Heat Island, The US EPA (3) performed measurements and observations of Sacramento between 1998-2002, revealing that there is an urban heat island of the order of an average increase in temperature in the city of 0.5-1°C (0.9-1.8°F) compared to the surrounding non-urban areas.

References

(1) Kauffman, E. Climate and Topography California Department of Fish and Wildlife

(2) Western Regional Climate Centre (WRCC), Climate of California

(3) U.S. EPA Urban Heat Island Pilot Project: Profile of Sacramento


Why is Vladivostock so cold, while Dalian is so warm? Over at SE History we're pondering the relation of temperature to Russian motives in establishing its Port Arthur colony in the late 1800's.

Wikipedia says that Vladivostok is cold due to the Siberian High. Meanwhile, in my answer, I repeated what I learned decades ago in my high school earth science class about ocean currents and why Port Arthur was warmer. (Port Arthur was located near the modern large city of Dalian)

Another astute SE member in the comments pointed out that trade winds are more responsible than ocean currents for weather effects.

What is the truth for these two places? (Note that we are asking more about Winter than summer, as both places would make fine summertime 19th century ports)

Also, was Vladivostok significantly colder 120 years ago than it is now?

Vladivostok (this is the spelling I've always seen) is located at 43º08'  N whereas Dalian lies at 38º05' N.   Five degrees make a difference.  Also, Vladivostok gets more influence from the cold Oyashio current than does Dalian.  For these reasons summers are warmer in Dalian and winters are colder in Vladivostok.


What is the distribution of global photosynthesis? Has anyone attempted to classify global photosynthesis? For example, what percentage of it occurs over land? What percentage comes from agriculture?

Same questions for total growth of plant mass.

There will of course be issues with definition and measurement. But any attempts at answers would still be interesting.

You are perhaps looking for primary production.

That is a measure for the total amount of biomass produced. Primary production is measured in two different ways, gross primary production, that is simply everything produced by the photosynthesis, and net primary production, that is the same, except for that the amount consumed by the plants is subtracted.

The estimates are about 115 gigatons of carbon per year of net production on land, and 55 gigatons of carbon per year for the ocean.

Roughly $\frac{2}{3}$ on land and $\frac{1}{3}$ in the ocean.

Cultivated land contributes to about 9% of the net production on land.
Read more about that here.

Below is the annual average of the net productivity of vegetation on land and in the ocean during 2002 (from http://science.nasa.gov/earth-science/oceanography/living-ocean/remote-sensing/ )


Can a hurricane with different spinning direction be formed accidently? As I know, due to Coriolis force, hurricane in each hemisphere can spin in one direction only. But I also know hurricane can be formed near equator, which colriolis force is weak. My question is, is it possible that a vortex spinned in opposite direction is developed into hurricane near equator?

No, a small vortex in a contrary rotation might develop into something larger if conditions were right, and there was no horizontal shear, but it wouldn't last long. The Coriolis force would work against it, and it would fizzle out.

Also, hurricanes don't form near the equator - there isn't enough horizontal rotation to kick-start it. Hurricanes initiate a little to the north or south of the equator.


Will Lithium batteries mean we have to exploit Co-Mn-Ni nodules from the seafloor? Co-Mn-Ni goes into some of the most efficient Lithium Ion battery chemistry. It is very convenient that Co-Mn-Ni is also readily available from the ocean seabed.

Polymetallic nodule contains various metals like copper, nickel, cobalt,
manganese, iron, lead, zinc, aluminum, etc. Of these, copper, nickel and
cobalt are of much importance and in great demand world over. In fact,
due to their extensive technological use these three metals are fast
depleting from the earth surface. Hence a world-wide research is
progressing on sea nodules as an alternative future source of these
metals

https://core.ac.uk/download/pdf/297714746.pdf
So long as Lithium-ion remains a core battery technology, it seems that polymetallic mining from the seafloor is inevitable. Are there other sources of these elements that can be used other than the seafloor? Can recycling offset this issue?

No it is not inevitable that the production and continued used of lithium batteries will mean the exploitation of cobalt, manganese and nickel nodules from the sea floor.
Sufficient quantities of these metals are obtained from conventional land based mines.
Nickel
Two types of nickel mineralization are currently mined: sulphides and oxides (laterites).
Sulphide nickel is easier to treat metallurgically than oxide nickels. Once mined nickel sulphides are initially processed using flotation plants. Nickel oxides usually need to be treated in autoclaves (high pressure cookers for minerals) and depending on secondary mineralization the autoclaves use either acid or ammonia.
The top ten nickel producing countries are:

The major laterite producers are: Indonesia, Philippines, New Caledonia, Brazil and Cuba.
The major sulphide producers are: Canada, Australia & Russia.
Australia also has significant reserves of nickel laterites (oxides).
The top nickel mining companies are:

There is plenty of land based nickel available before sea bed nodules need to be mined for nickel.
Manganese
Manganese is the 12th most abundant element in the Earth’s crust. About 18.5 million tonnes of manganese are produced globally annually, from
South Africa   6.2 Mt
Australia      3.0 Mt
China          2.9 Mt
Gabon          1.8 Mt
Brazil         1.0 Mt

There are significant known resources of land based manganese for many years to come.
Cobalt
Cobalt is currently mined by eight countries, with the Congo being the main producer:
Congo           100.0 kt
Russia            6.1 kt
Australia         5.1 kt
Philippines       4.6 kt
Cuba              3.5 kt
Madagascar        3.3 kt
Papua New Guinea  3.1 kt
Canada            3.0 kt

Again, there is sufficient production and resources of land based Cobalt to not require the exploitation of sea bed nodules.


Why are hail storms always brief? It can rain all day, and it can snow for hours on end, but hail always seems to fall for short periods only. Why don't you get prolonged periods of hail?

Hail is created within a thunderstorm by updrafts that carry water droplets to an altitude where temperatures are lower than the freezing point for water (32°F or 0°C). This freezing process forms a hailstone.  This process can repeat itself each time increasing the size of the hailstone until the hailstones become too heavy to be picked up again.

The creation of hail requires high wind speeds that may only be sustained in the storm for a brief period of time.  Longer periods of hail are possible but typically require large strong storms such as tornadic supercell thunderstorms.

References:
   How hail is created.
   Size of Hail vs wind speed


regeride netcdf file to template netcdf file resoulution how can I regrid my NetCDF file into another NetCDF file resolution?
change the file number 1 or 2 or 3 (file1.nc,file2.nc,file3.nc ) into resolution template file (wrfinput_d01.nc) . I have attached my files(number1,2,3 and template file)
wrfinput file:
https://drive.google.com/file/d/1j6SrSOpbrkWXDl2_WK-MGwcm0zKOXV_x/view?usp=sharing
my files 1,2:
https://drive.google.com/file/d/1-o7TUcPmzsuOYhpry92ROHQuLA1Z5N-x/view?usp=sharing
cdo griddes wrfinput_d01.nc > NEW_GRID_DEF
cdo remapbil,NEW_GRID_DEF file1.nc output.nc
I've done it by cdo, but it shows error
Error (cdfGetSliceSlapDescription): Internal error, variable evi has an unsupported array structure!
What shall I do to solvr this problem?
thank you!

Disclaimer: I have not looked at your particular files.
You can regrid the data in Python using the NetCDF-4 module. If you want to interpolate vertically, you can use the WRF module. And for horizontal interpolation, you can use the Scipy Interpolation module.
Here is how you would go about this regridding.
Let $(\psi_1,\phi_1)$ be the  latitude and longitude of the original data and $(\psi_2,\phi_2)$ of the regridded data. Let $y_1$ be your original data, and $y_2$ be the regridded data.

Open the original dataset.
Create an interpolation function trained on your original data $f(\psi_1,\phi_1)$ for the data you will regrid.
Using the NetCDF module, declare the dimensions, variables, and necessary metadata for your regridded dataset.
Use your interpolation function, so you get $y_2=f(\psi_2,\phi_2)$.
If necessary, interpolate to your required vertical level.
Repeat starting on step 2 for every time for every variable that you want, at every vertical level (if required).


How to install WRF on a Mac? I would like to install the WRF model on my Mac computer.

Where can I find up to date and step-by-step instructions on how to install it?

The WRF Forum here describes some testing using a Mac, and they report success with 2 setups:

Mac 64-bit running OSX, with Intel chips, g95 gcc version 4.0.3 (g95 0.92!)

Mac 64-bit running OSX, with Intel chips, PGI pgf90 10.3

There is also some additional information on installing ARWpost on a Mac found on the WRF Forum.

Separately, there is a blog dedicated to your question here and instructions on github here.  Also, if you see the tutorial here, you can see a method that requires you to install Xcode.


Measurement of groundwater levels There are numerous measurement systems available for measuring groundwater levels (head / stage). What period of time should measurements be collected in order to draw solid conclusions about the average groundwater level, min/ max, trends, etc?

I would reckon at least one year to take account for seasonal effects, although longer would be preferable.

Long measurement periods (several years) are often unwanted, due to the need for fast results. So the optimum between research duration and solid conclusions lies somewhere in between I presume.

EDIT:
There is a factory which is investigating the possible use of groundwater for their industrial process. Enviromental law dictates that the effects of the substraction on the long term groundwaterlevels have to be investigated.
The first question then becomes: what is the current long term variability in groundwaterlevel? Only then we can continue with answering what the effects of the substraction will be.

Terminology

The term "average" isn't a specific term.  Are you talking daily average or annual average?  The question "What is the average?" begs the question of "Across what timeframe?"

To explain that point, let's look at a chart:

This is the groundwater level of a well in Georgia (US).  The "Average" for this well in December 2003 would be 176.9 feet below ground level.  However, for the year 2003 altogether, it would be around 177 feet below ground level.  For the previous decade, the average would be around 175 below ground level.

Timeframe

To answer the question, the best timeframe is the largest timeframe you can afford to gather.

If we look at the Raw data for the above chart, what we see is that for the year 2003, the groundwater levels went up!  If you only gather one year of data, this well would show a dramatic rise in water levels, which would clearly not be accurate.

If you extended the search back five years, you would see a large downward trend.  March 1998 shows 173 feet depth and March 2003 shows 178 feet depth.  This trend indicates a dramatic loss of water volume.  However, without a larger timeframe, it's impossible to tell if this is just a negative trend or a cyclical one.

Even in the chart above, the mid-1960s may just have been a high point in the water table.  (Indeed, Georgia saw 15% more percipitation in 1967 than is historically average and 1999-2001 were historically low.)

Summary

The best timeframe to gather data is the longest timeframe that you can get.


What exactly is a model grid? I have just begun reading some notes about modelling and can't seem to understand what a 1°x1° grid or a 2.5° grid or 1/4° grid is. I know it is something related to the resolution of the area over which the model is run. But when a line goes like this, 'the values are archived on 2.5° grid', what exactly should I make of it?

A model is a simplified representation of a system.
Rather than try to model everything down to the microscopic level, or as vectors, we often aggregate phenomena across geographic areas, to simplify the computation a lot, and to lose only a little accuracy and precision.
So if I want to model the UK, which roughly spans 8°W - 2°E, 50°N - 60°N, that's an area of 10° x 10°. I can split that up into 400 cells, each 0.5°x0.5°. So, starting in the south-west corner, I model the area 8°W-7.5°W x 50°N-50.5°N as a single homogenous area, with no variation within it. And the same for the other 399 cells (8°W-7.5°W x 50.5°N-51°N and so on all the way through to 1.5°E-2°E x 59.5°N-60°N). That would be a 0.5° grid.
Note that the gridcells do not overlap at all, and they completely cover the area of interest. (in mathematical terms, they are exclusive and exhaustive - every point of interest is in exactly one gridcell).
That can create vast amounts of data - particularly if I'm doing this for the whole world, at second-by-second resolution for ten years, for a few hundred variables of interest. And no one will ever need that amount of precision from my results. So I aggregate up to a more coarse grid: so that I take 25 grid squares that form a larger square, and aggregate each of my statistics of interest in them. So now my area 8°W-7.5°W x 50°N-50.5°N is just one of 25 cells in a square in the coarser grid which spans 8°W-5.5°W x 50°N-52.5°N, and I report statistics for 16 cells in a 2.5° grid, rather than 400 cells in a 0.5° grid.
Exactly how that aggregation is done, will vary for each statistic:

If it's a minimum overnight temperature, then I'll take the minimum across all 25 0.5° gridcells, to give the minimum overnight temperature for the 2.5° grid cell that consists of those 25.

If it's an average rainfall, I'll take an average.

If it's insolation per unit area, and I'm interested in insolation per unit land area, then I'll take a weighted average of insolation, weighting by the proportion of each 0.5° gridcell that contains land.

And then I'll have modelled on a 0.5° grid, and archived on a 2.5° grid
Here, for illustration, is Britain in a 100km grid:
 source
If I wanted to aggregate that to a 200km grid, then the four squares in the bottom-left corner, SQ, SR, SV, SW, would be combined into a single grid cell. And SS, ST, SX, SY would be combined into another grid cell. And so on.


What is the best way to accurately measure wavelength between crests on a pier? What is the best way to accurately measure wavelength between crests from a pier?

One simple way is to measure the period $T$ by timing the arrival of each  crest and estimate the wave speed $c$ from the depth of the water column $d$. Then one can compute a wavelength $\lambda$.

Assuming the waves are shallow-water waves ($d/\lambda < 1/11$) and linear (modeled by a sine or cosine function), the error should be no more than 10% (is that accurate enough? It gets more complicated otherwise).

Since we assumed these are shallow water waves they are nondispersive and the speed is simply $c = \sqrt{gd}$, where $g$ is the gravitational acceleration.

Thus, the wavelength is
$$\lambda = c\ T=\sqrt{gd}\ T\,.$$


Curious natural patterns on the surface of basalt blocks that make up the sidewalk During one of my walks through the city streets, I noticed that some basalt blocks that make up the sidewalk have in their surface some very curious natural patterns:

The photos above was taken at the Porto Alegre city, Brazil. (Coordinates: 30°01′59″S 51°13′48″W)

What are these fractal-like patterns? How and when are they formed? Is it some kind of organic formation? Is it living being or some kind of fossil?

These are most likely manganese dendrites. It is not a fossil, and not organic. These usually form in cracks in rocks, and most likely this slab was broken along an existing crack.

You can read more about it now: https://www.mindat.org/min-26645.html

One comment: this is not basalt. Basalt is black. Most likely some form of limestone.


Is this ice cover real - and what circumstances are required to make it? The title says it all...

I assume it requires very slow freezing sheltered from the wind. Undercooled water, maybe?

A Reddit thread talks about Delaunay triangulation, but that does not explain how it forms.
Many sites claim it is in Switzerland without further info.
A comment in another Reddit thread states It happens when there is a significant change in gravitational force during the freezing process.
Also, there was a lot of water vapor hovering just above the surface during a rapid freezing process, but I doubt that gravitational effect.

BTW While researching this I found this beautiful page 18 Beautiful Frozen Lakes, Oceans And Ponds That Resemble Fine Art - several of the pictures there would merit a separate question ;-)

It has a similar picture claiming to be from a frozen pond In Southern Oregon, USA*:

* But a Google image search finds claims of Switzerland, again.

Yes, it is real. Whoever took the photo, congratulations on a very fine image.
I have never seen this texture on such a scale, but something similar can be achieved in the laboratory by creating a bubble membrane of pure super-cooled water and blowing a few dust nuclei onto the surface. A comparable geometry appears very rapidly. The size of the ice crystals are difficult to judge without any feature to give scale, but they seem to be exceptionally large trigonal terminations. We have all seen snowflakes of hexagonal symmetry, but under metastasble conditions with rapid crystal growth, stacking disorders tend to occur in the crystal lattice, yielding a trigonal rather than hexagonal symmetry. See, for example, The ice phases of water. This is an unusual polymorph of ice, and I cannot see how it could be achieved except through sudden seeding of very pure water. Perhaps stratified freshwater lakes, in still air, on a very cold night, with a surface of previously melted ice (i.e. pure water), and subject to a sudden windy gust of nucleation particles would provide the right conditions?


Help understanding radar data in subglacial lake complex beneath Devon Ice Cap The BBC News article Isolated lakes found beneath Canadian ice sheet links to the open access Science Advances article Discovery of a hypersaline subglacial lake complex beneath Devon Ice Cap, Canadian Arctic.

The discovery is based on radar "soundings" of the Devon Ice Cap in Nunavut, Canada.

I'm having trouble understanding the nature of the evidence for liquid water as stated in the article. I understand that there will be a relatively strong reflection at an interface where the dielectric constant changes, and that this is expressed in dB in some of the plots, but I don't understand the blue "blobs" or the axis label Spec. content shown.

I've cropped some of the plots and rearranged them below to help highlight the "blue blobs" below.

Also it looks like the potential lakes are associated with dips in the underlying bedrock contour, but I can't tell if the lakes are sitting on top of the bedrock, or at an intermediate elevation above it with ice both above and below them, and I don't understand at all what the axis label Hydrolic head (m) in other plots means. And why does the top surface of the ice look relatively flat in the radar images, but rise and fall hundreds of meters in the ice surface and bed plots? Did the plane fly at a fixed distance with respect to the top ice surface rather than a fixed elevation?

below: Figure 1 from here.

In the figure:
The top panel is the radar gram measured in depth below ice surface (m). It is flat because when you do the measurement it starts from the surface which is 0m. The distance from the aircraft to the surface would have been relatively constant but the plot starts from the air-ice boundary at 0m.

The middle panel is a cross section measured in m above sea level showing the ice surface and bedrock (black), the hydraulic head (blue) relative to sea level. The hydraulic head is calculated by working out the pressure of the ice sitting on top of the bedrock. It is basically the water table of the ice sheet. Whats important is the differences in hydraulic head (gradient) which can tell you if the water is moving or not.

The bottom panel is the reflectivity (black) and specularity (blue) of the basal surface. Specularity is a ratio of reflected to scattered energy. Simply put, high reflectivity means the boundary is wet and high specularity means the boundary is flat (Ice-Water), and low specularity means its rough (Ice Rock).


Do solar storms affect Earth's weather? Solar storms have a direct effect on Earth, creating charged particles.  What are the direct and indirect effects of large solar storms on Earth's atmosphere?  Do solar storms affect Earth's weather?

Those charged particles you mention are not created by any kind of interaction with the Earth, but rather the term solar storm is used to refer to the temporary disturbance of Earth's magnetic field caused by bombardment of the charged particles which originate from the Sun. The stream of particles flow outward from the sun through the solar system(The particles escape from the sun because its outer atmosphere is very hot, and the atoms there move too rapidly for the sun's gravity to hold onto them).

However, these charged particles are different to the usual  solar radiation (visible and ultraviolet light) that penetrates Earth’s atmosphere and warms the surface. Most of the charged particles that are aimed at the Earth are deflected back into space by Earth's magnetic field, although a small amount may enter the thermosphere (upper atmosphere). This may cause some warming in the thermosphere, however, carbon dioxide and nitrogen oxide, coolants in the thermosphere, absorb the energy and then re-radiate heat back into space.  A small fraction of the extra heat from the solar flare radiates to layers of the atmosphere below the thermosphere, but it is insignificant compared to the normal amount of heating the lower layers of the atmosphere already experience from normal solar radiation.

References:

Bates, D.R., 1969. PHYSICS OF THE UPPER ATMOSPHERE. QUEEN'S UNIV BELFAST (NORTHERN IRELAND) DEPT OF APPLIED MATHEMATICS AND THEORETICAL PHYSICS.

Mlynczak, M.G., Martin‐Torres, F.J., Crowley, G., Kratz, D.P., Funke, B., Lu, G., Lopez‐Puertas, M., Russell, J.M., Kozyra, J., Mertens, C. and Sharma, R., 2005. Energy transport in the thermosphere during the solar storms of April 2002. Journal of Geophysical Research: Space Physics, 110(A12).


Why is runaway global warming a possibility despite all current fossil fuel deposits once having been in the atmosphere? Disclaimer: this question is not about climate change or global warming in general, and does not try to dismiss general human-caused global warming.

The concept of runaway global warming depicts a scenario where the climate enters an unstoppable positive feedback loop, and this scenario is often depicted to happen before all fossil fuels have been used up. This topic often comes up in discussions about how long fossil fuels will last, with a warning that the planet would be uninhabitable if (or before) all fossil fuels were used up.

However, all these fossil fuels were once in the atmosphere in the form of CO2, before they ended up absorbed by plants. Why didn't Venus-like conditions form back then, and since they didn't, why could the result be different in the future?

The largest reservoir of carbon is the rocks (sedimentary rocks) which reach into the order of the million gigatons (Gt). Over millions of years, part of that carbon have been released to the atmosphere by volcanoes. However, the amount of carbon in the atmosphere remains fairly stable because chemical erosion of rocks and plants capture that carbon excess and put it back in the geological reservoir.
There is about 4.700 Gt of fossil fuels, and the Atmosphere contain only 760 Gt of carbon.
So, if we burn all fossil fuels slowly, lets say over many millenia, there is not a problem, plants and erosion will keep the balance.
However, if we burn it quickly, let's say over few centuries, the greenhouse effect can be so strong that can kill many plant ecosystems and rise sea level, therefore reducing the surface area of rocks exposed to chemical weathering. Both effects would reduce the capacity of the system to restore the equilibrium, therefore it could take many millenia before all that carbon is returned to the geological reservoir so that temperatures can return to normal.
An extreme runaway case, like what happened in Venus, is unlikely to happen on Earth, but we don't really know. If temperatures ever reach to the point that oceans boil, then, there is a possibility that Earth might become like Venus forever.


Sun Appearing to have Stopped Shining What could have caused the Sun to appear to have stopped shining from noon 'till 3 in the afternoon? According to the Bible, at Jesus' crucifixion, darkness covered the land because the sun stopped shining from noon 'till 3 in the afternoon. What could have caused this phenomena? I know it couldn't be a solar eclipse, since they don't last that long and there weren't any on April 3rd of 33 AD (date of Crucifixion, see here for why). So what known phenomena might have caused this said darkness?

This is arguably a theological rather than science-based issue, but since it is cross-cutting I will answer it. No, there is no possible mechanism for the sun to cease shining for three hours, but neither is there any such imperative implied in the original Greek text, which requires only a relative darkening of the sun. You are correct is recognizing that this was no eclipse. It may be noted that comparable 'darkness' is implied in other Biblical accounts, such as in Exodus, Joshua and Amos. Nowhere in these four texts is total darkness either stated or implied, but naturalistic explanations (i.e. scientific) - which in no way detract from the spiritual intent of the text - may be eminently applied to correct textural understanding.

As in so many modern misunderstandings, the apparent conflict between science and Biblical theology entirely disappears when the Bible is read sensibly from the appropriate perspective. The Bible is not a modern Greco-Roman literalist quasi-scientific text, and it is absurd to treat it as such. the Bible is basically a spiritually-inspired collection of 2000 to 3000 year-old Hebrew texts, written in the parabolic allegory-rich idiom of ancient Jewish culture. For all that, it is no less accurate in spiritual wisdom - but radically different from the 21st century world views.
Not only is it literature from a different culture, we also don't have any of the original texts! You are arguing on the basis of a modern British translation of a Greek translation of presumed Aramaic literature, which itself may have undergone several cycles of editing (as in the Noah story, which has at least four cycles of precursor literature).  Subtleties of meaning are lost in each stage of translation, so before drawing over-literal assumptions, first at least check the nuances of the earliest Greek translation. Had you done that, the question of absolute darkness would not have arisen, and partial darkness would more than adequately have proved correct - such as a sandstorm (as 'A child of God' suggested) or a super-cell storm cloud - which does occasionally occur in the Israeli highlands. Such superstorm events may indeed seem to be like night if they occur only minutes after the intense bright Mediterranean sunlight of the Jerusalem highlands.

Many Christians get hot under the collar when scientific explanations are applied to miracles such as this. It need not be so. It is not a case of "look at God doing the impossible, again", but of placing due emphasis upon the true miracle, which is the exquisite timing of the event in support of God's unfolding plan.


How do cold air masses reach South America? I've heard meteorologists say that the cold air masses that reach South America during the winter months don't originate in the polar region.  I always thought they crossed the strait of Drake and reached the continent at Tierra del Fuego. From there, depending on their strength, they might reach very low latitudes.  If these cold air masses don't come from Antarctica, where do they come from?

The Pacific coast of South America is affected by the Humboldt Current, a wind-driven "upwelling" system running counterclockwise that is cold and windy but nutrient rich..

The "six o'clock" position indeed touches Antarctica, and then the right edge of the "clock" reaches Chile, Peru, Ecuador, and Colombia, as far north as the Galapagos Islands near the equator, making those areas cooler than equivalent latitudes on the Atlantic coast.

The reason the currents don't "originate" in the Antarctica is because they are part of a circular system that spans a whole ocean in the southern hemisphere. But they "make stops" there.

The ocean and atmosphere mutually interact to create a "coupled" system that brings about the so-called ENSO El Nino (La Nina) Southern Oscillation, with its characteristic weather changes.


Can one assume atmosphere (primarily nitrogen) is heavier than water (vapor)? Water capacity decreases as the atmosphere cools.  However, a denser cold atmosphere sinks while a warm, lighter atmosphere rises.  Can one assume atmosphere (primarily nitrogen) is heavier than water (vapor)?

Yes, humid air is less dense, but temperature and pressure are much bigger factors in the density of air.   Temperature and pressure are what drive updrafts and wind speed and direction much more than watervapor.

Absolute Humidity, or, the mass of water vapor in air, is a smaller factor.  See here for more detail.

Temperature

a 5° change can vary air density by almost 2%.   That means that the Sahara desert (35° c) and the North Pole (-25°), with humidity and pressure being equal, the air in the North Pole is about 24% denser.  Locally, if we assume a 15° variation daily or 25-35° variation yearly, that's 5%-14% locally, give or take, based on temperature.

Pressure

Per Avogadro's law, a 10% increase in pressure, everything else being equal, corresponds to a 10% increase in gas density.  10% variation in air pressure is quite a bit (unless you're climbing a tall mountain).   Locally, 10% variation in air pressure is rare, unless you're looking at records.  Daily/Weekly highs and lows in barometric pressure might vary air density by 3%-7%, ballpark estimate.   See some record high and low pressure here.

Water Vapor

Again, using Avogadro's law, if you add 1 water vapor molecule to the atmosphere, and maintain temperature and pressure, you need to push one other gas molecule out of the set volume, so per cubic meter, increasing humidity does displace some of the heavier gas molecules, so there is some lightening of the air, but the effect is quite small.  Water-vapor has a molecular mass of 18.  Our Oxygen-Nitrogen atmosphere has an average mass of about 29.   But Water Vapor rarely takes up more than 1% of the atmosphere, perhaps 2% in hot climate, 3% in very hot, but that's 3% of the air with a 38% decrease in mass, resulting in a net change of less than 1% in the most extreme conditions.  You'd be unlikely to see a variation of as much as 1% density by water vapor alone, outside of a very rare event, like a thunderstorm over a desert.

There's other factors for wet air, such as the evaporation of water has a cooling effect, so as water evaporates, the local atmosphere cools, so mid day when sunlight driven or wind driven evaporation is at it's peak, it's not quite so neat and tidy as to say that evaporation makes the air lighter, cause the cooling effect of evaporation may actually make the air slightly more dense, not less dense.  It's only less dense if you keep everything equal, but the cooling effect of evaporation doesn't keep everything equal.

That said, however you look at it, water vapor is likely an order of magnitude less important for air-density than temperature and pressure.   It's relevant enough to pay attention too if you want to study air-density carefully, but temperature and pressure are the two big factors.

A possible exception to this is when there's fog.  Fog contains tiny water droplets which are small enough to be suspended in air by wind, and those individual droplets are much denser than atmospheric water-vapor.   foggy air, under the right conditions might be the most dense air, but in a sense, that shouldn't count, it's like counting sand in a sandstorm as part of the mass of air.

((Corrections welcome if I made any errors)).


What determines the direction of wind What determines the direction that a wind blows?
I understand that the nature of my question is immensely broad but I see no other way to phrase the question.

The question surfaced in my mind when I was checking out the windmap for the South East Asian region as I was monitoring the haze conditions there.

snapshot 1602 GMT+8, 18 Sept 2019

moved my extra thoughts to comments so I don't bog down the actual description

Sunlight (heat), planetary rotation, and the shape of the land in passes over.

Prevailing wind is mostly caused by hadley cells, or the masses of air move due to the thermal difference between the pole and the equator as well as the surface of the earth and space. Thus it is the interaction of two different thermal convection cells. this moment twists into cells due the coriolis effect and the earths rotation.

Source

these cells can be somethat disrupted on the local level by things like mountains.


Why do major earthquakes keep happening in Lombok recently? So I've been following the situation in Lombok, Indonesia from both online news and friends on the ground. One thing that I can't seem to figure out is why there are so many earthquakes over an extended period of time?

Is it common for there to be earthquakes for a whole month and of high magnitudes too in seismic zones?

I have read that there can be aftershocks but I don't think that all these quakes can be characterized as aftershocks of the initial quake? These quakes seem to be independent quakes and not a continuation of aftershocks.

The major 3 have also been progressing from the south to the northwest to the northeast of the island as though it is following some pattern. So is this a sign that an underlying seismic development such as a major shift in tectonic plates or a new volcano formation will take place?

Please explain it to me like I'm 5.

Yes, it is quite possible to have a number of earthquakes over an extended period of time.

The phenomenon is actually quite common. Some famous sequences are: New Madrid in 1811-1812, Eastern California Shear Zone (1992-1999), Italy (2009-2016), New Zealand (2010-2011) etc. These are all areas where rates of seismicity are relatively low so a number of events in quick succession (as you saw in Lombok) is not due to pure chance.

The idea of one earthquake triggering another on a neighboring or nearby faults is not new.


Considering how old the Antarctic ice cover is, why isn't it much thicker? The Wikipedia article on the subject of the "Antarctic ice sheet" says that:

  The icing of Antarctica began in the middle Eocene about 45.5 million
  years ago and escalated during the Eocene–Oligocene extinction event
  about 34 million years ago.

The article later says that:

  Ice enters the sheet through precipitation as snow. This snow is then
  compacted to form glacier ice which moves under gravity towards the
  coast.

However it looks like, from the photos of Antarctica, that this transfer of ice to the coasts is not happening everywhere on Antarctica. Rather, many places seem to be under a perpetual ancient ice cover.

If the precipitation was recycled at a continuous rate everywhere, it shouldn't be possible to obtain a 1.5 million year old core sample (according to an article in Nature) or structures such as Vostok Station that covers an ancient lake.

A similar location is Dome F that according to Wikipedia has a yearly precipitation of about 25 mm (millimeters; approx. 0.98 inches).

So the question is, if we have a 25 mm precipitation per year for even just 10 million years, we should have an ice cover that's 250 kilometers thick. Or if we had it during just a 5 million year time span, it should give us an ice cover that's 125 kilometers thick.

Reasonably we could allow for some compression of the ice but still keeping in mind that we are not talking about a neutron star here.

How come the current Antarctic ice cover is just a couple of miles thick?

Ice floats with gravity towards lower elevation, the flow continues until the base of the ice sheet becomes floating and the ice forms an ice shelf or icebergs.

Due to the subglacial topography, basal melting and mass balance, the flow velocities vary over a large range, faster outflows are glaciers. The pattern is somehow similar to how rivers transport rainwater towards the coasts.

The thickness of the ice sheet is controlled by a complex relation between accumulation, ablation and compaction.

Flow velocities from MEaSUREs (NASA), plotted from Quantarctica

The ice velocity map shows, that some areas are unaffected by the flow towards the coast. Some of these areas are ice-free. Also at some inland locations, as Vostok, the velocity is very slow. Also, note that this is the surface velocity. At the base, the ice flows slower or not at all.

To investigate the mass balance further, I recommended you to download Quantarctica and look closer at the datasets. Shepherd et al (2012) is a good first read about the methods used to estimate changes in ice mass and the present changes.


What determines the differences in results between various weather models? It's often said that the ECMWF model is more accurate than the GFS. But what are the reasons why? Also, how does the NAM compare with these two and other forecasting models?

Everything.

Parametrizations
Spatial (horizontal and vertical) and temporal resolution
Initialization
Dynamical core
Handling of some complex non-linear physical processes (i.e. snow)
Topography representation
How the mathematical equations are numerically implemented
The computer used
etc...

These are some of the main differences between 2 given numerical weather model.

With that being said, the same model will give different results when run on a different computer (Hong et al. 2013).

Hong S-Y, Koo M-S, Jang J, et al. (2013) An Evaluation of the Software System Dependency of a Global Atmospheric Model. Mon Weather Rev 141:4165–4172. doi: 10.1175/MWR-D-12-00352.1


Speed of tectonic plate movement I'm doing some research about tectonic plates and at which speeds they move and in which directions, because I'm writing a program to simulate the movement of the tectonic plates and also calculate where they were at previous times and where they will be at some time in the future.

For this reason, I'm trying to find some good information about the speeds of the movement of the tectonic plates over the years and in which direction they were moving. I found one set of estimates here but I'm not exactly sure how accurate they are. I also found estimates on the Wikipedia pages for each of the plates, but it doesn't agree with the first estimate and I also want to know how significantly these speeds are changing over time (I assume the movement of the plates is not linear).

Finally, the first site doesn't give in which direction they are moving and Wikipedia does, but I don't quite understand it - it says, for example "west relative to the African plate", and I'm not sure what exactly that means.

Any help would be appreciated here. Thanks!

Check out the USGS seismic data maps for each area of interest. For example,
http://walrus.wr.usgs.gov/tsunami/samoa09/
gives you the relative speed and direction of the Pacific Plate relative to the Fiji-Tonga-Australian plate. The directional arrow on Figure 2 gives the mean relative motion in mm/year.


Plotting wind barbs in python I'm trying to plot wind barbs in basemap and I have two separate netCDF files.
Here is what I am trying to produce, or something like it:

How do I read a uwnd variable in a netCDF file in python?

from mpl_toolkits.basemap import Basemap
from netCDF4 import Dataset, date2index
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from IPython.display import Image
from IPython.core.display import HTML
import netCDF4 as nc
plt.clf()
nc_file = '042711_V.nc'
nc_file2 = '042711_U.nc'
V = Dataset(nc_file, mode='r')
U = Dataset(nc_file2, mode='r')
print U.variables

This verifies that uwnd is indeed a variable, but why won't it read?

OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Variable'>
float32 lat(lat)
    units: degrees_north
    actual_range: [ 90. -90.]
    long_name: Latitude
unlimited dimensions:
current shape = (73,)
filling off
), (u'lon', <type 'netCDF4._netCDF4.Variable'>
float32 lon(lon)
    units: degrees_east
    long_name: Longitude
    actual_range: [   0.   357.5]
unlimited dimensions:
current shape = (144,)
filling off
), (u'time', <type 'netCDF4._netCDF4.Variable'>
float64 time(time)
    units: hours since 1800-1-1 00:00:0.0
    long_name: Time
    actual_range: [ 71870952.  71870952.]
    delta_t: 0000-01-00 00:00:00
unlimited dimensions: time
current shape = (1,)
filling off
), (u'uwnd', <type 'netCDF4._netCDF4.Variable'>
float32 uwnd(time, lat, lon)
    long_name: u wind
    valid_range: [-99999.  99999.]
    actual_range: [ -26.84999084  377.20001221]
    units: m/s
    add_offset: 0.0
    scale_factor: 1.0
    missing_value: -9.96921e+36
    precision: 99
    least_significant_digit: 99
    var_desc: u wind
    dataset: CDC Derived NCEP Reanalysis Products
    level_desc:  500mb Pressure Level
    statistic: Composite
    parent_stat: Other
unlimited dimensions: time
current shape = (1, 73, 144)
filling off
)])

KeyError: 'uwnd'

I'm trying to plot some widnbarbs on the map. i have successfully gotten it to read the time, longitude, and latitude, bu what does the uwnd mean and how do I read it into the numpy format?

Here is the site I found on how to read netCDF files:
http://www.hydro.washington.edu/~jhamman/hydro-logic/blog/2013/10/12/plot-netcdf-data/

Your uwnd variable holds 32 bit floats and has shape (1,73,144) corresponding to time, lat, lon and is located in the Dataset you have called 'U'.

One way to put this in a numpy array is:

uwind = np.zeros((lat,lon), np.float)
uwind = U.variables['uwnd'][1,:,:]

The first line sets the size of the uwind array, which is helpful from a performance standpoint and the second loads the data from time 1 into the array.  lat and lon above are your grid dimensions 73 and 144 and np is the numpy module.

The u wind is just the east-west component of the wind.  The north-south component will be in the v wind variable.  You will need both to plot the wind.  To plot the barbs you will need to setup arrays for your x and y coordinates which will depend on lat,lon and possibly your map projection.  once you have an x and y array as well as uwind and vwind you can plot the wind barbs using matplotlib as:

plt.barbs(x, y, u, v)

and can customize their look with various options. The plt reference is the matplotlib.pyplot module.

For further reading, here are some examples of plotting netcdf data from a friend of mine at CSU.


What are these features in the Tombouctou Region? I’ve recently been very interested in globe-trotting via Google Earth. At first I was using it to plan out hikes to possible mineral deposits locally, or potential fossils/ archeology sites. However I soon became sidetracked in the Tombouctou Region of Mali. I stumbled across an outcrop of rock in the middle of the desert. It appears to be brilliant blue.

I am wondering if this is some kind of satellite imagery to detect heat or moisture? Or is this the actual colour of rock/mineral?

Those blue colors are basically artefacts due to image processing.

The last one in Bing Maps doesn't look blue at all. It's just somehow slightly darker with some trees or large animals. The local change of color might be caused by humidity (maybe a seasonal pond) or animals, or both - if animals come to drink. Interestingly, there are similar shapes in the area and Bing Maps traces paths converging on them. I don't know how Bing gets those paths (I can't see them in the aerial imaging) but they are consistent with animal paths leading to ponds.

And the second one is just the bed of a dry lake, with salt pans.

The first one lacks coordinates, but we can assume it's just a another color enhanced rather normal piece of desert.

Edit

To actually answer the question "I am wondering if this is some kind of satellite imagery to detect heat or moisture? Or is this the actual colour of rock/mineral?", as requested by comments:

The answer is "No, it is neither the colour of rock/mineral or a heat or moisture detector. Blue color is just an artifact due to image processing, as can be seen from comparing those Google Maps images with linked Bing Map images of the same places, which aren't blue at all".


Ordered arrangement of clouds I happened to take a photograph of a region of clouds ,and it seems to have a rather ordered arrangement ,if one looks towards the left of the image. Would there be a physical explanation for this or is it just chance?

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


If ALL the ice melted, what percentage of the Earth's surface would be water? At the present time, about 70% of the earth's surface is liquid water (perhaps that includes floating ice around Antarctica, and the Arctic Ice cap).

What percentage of the Earth's surface would be water if ALL the ice had melted?

I've seen illustrations of the new shoreline, and I've seen estimates of the rise in sea level, but no answer to my question.

As @gerrit commented, a precise calculation would need to incorporate a litospheric model to account for isostatic post-glacial rebound. But the "blurry approximation" you want, can be obtained based only on topographic data. This approximation would be reasonably accurate if the melting of the ice happens quickly.

For this, we need topographic data of the current surface, and of the bedrock topography. That's the bare ground below the ice. The only source of such data at a global scale (at least that I'm aware of) is ETOPO1, at a resolution of 1 arc-minute (roughly 1.8 km).

A visualization of current surface data from ETOPO1, using an equal-area projection (Eckert IV) and setting the sea level at 0 m looks like this:

And if we measure sea and land areas, it says that currently, a 70.97% of the global surface corresponds to oceans, pretty consistent with the figure you have. Be aware that this simple approach does not consider lakes or rivers, only oceans. It would also misinterpret below sea level dry land as ocean. However, all those factors are arguably minor contributions at a global scale.

Now, if we use the bedrock version of ETOPO1, and set the sea level at 66.5 m, consistent with the best estimates of water stored in land ice. The globe would look like this

In this case, if all the ice melts, the fraction of the Earth's surface covered by water would be 75.18%. Equivalent to an increase of 4.21% relative to the current value.


When metamorphic rocks undergo pressure, how are they changed? When metamorphic rocks undergo pressure they are changed, but how are they changed? Do they become fragments of rocks? Doesn't that mean that the difference between sedimentary rocks and metamorphic rocks (when talking about fragment rocks) is that sedimentary rocks undergo weathering, which makes them fragments and metamorphic rocks undergo immense pressure which makes it into fragments?

Metamorphic rocks are formed when a rock (sedimentary, igneous or a previous metamorphic rock) comes under high pressure and/or temperature. Pressure and temperature forces the atoms to form new minerals and thereby a new kind of rock. It's not necessary fragmented, but the rock rather morph through recrystallization into a new state in response to the pressure and temperature as the material reorganize.

Recrystallization doesn't remove any material, it's only a physical reorganization to compact the rock. If some change of the chemical composition occurs, it's called metasomatism. In metasomatism atoms are actually moved from one part of the rock or formation, often with water involved.

A good petrologist can usually determine what kind of parent rock, protolith, that have been metamorphosed and to what degree, but sometimes it can be difficult do recognize the difference. At lower degrees of metamorphism, features of the original rock is preserved. E.g. you can see ripples and even fossils in slates or deformed structures from the source rock, but at higher degrees of metamorpism it's increasingly difficult to imagine how the parent material looked like. Gneiss formed from sandstone and granite can look very similar as the lihology represents the conditions of metamorphism, not the protolith.

It can also be difficult to recognize a metamorphic rock. A weathered slate and shale or even granite and gneiss can appear very similar in the field. However, the processes to form the rocks are different and with a closer look at the minerals it can be possible to understand what kind of process that formed the rock.

    (1, Blueschist facies
    2, Eclogite facies
    3, Prehnite-pumpellyite-facies
    4, Greenschist facies
    5, Amphibolite-facies
    6, Granulite facies
    7, Zeolite facies
    8, Albite-epidote-hornfels facies
    9, Hornblende-hornfels facies
    10, Pyroxene-hornfels facies
    11, Sanidinite facies)

The temperature and pressure determine what metamorphic rock you get, but the chemical composition is inherited from the protolith. Degrees of metamorphism are called metamorphic facies. This diagram shows at what depth and temperature a particular rock is formed.

Igneous rocks are formed when a melt hardens to crystals and sedimentary rocks are formed from sediments. All rocks weather if exposed to water and air, and weathered material is transported by rivers to oceans where sandstone can be formed from the sand at the beach and shale from the finer sediments further away from the coast. Igneous rocks can be formed as hot magma intrudes the crust and slowly cools down and minerals are formed or form lava at volcanoes. This is known as the rock cycle and is one of the fundamental, but complicated, concepts in geology. You can read more about the rock cycle e.g. here to learn more about rock types and how they are formed.


How to speciate EDGAR emissions for CBMZ? I have an EDGAR emissions data-set. I would like to speciate the VOC emissions for CBMZ mechanism. Could anybody help? thanks

I used the R package eixport (Ibarra-Espinosa et al., 2018) to download EDGAR data (5 and spec voc 4.3.2) and then include several speciations for chemical mechanisms according to Carter (2015) and Lopez-Norena et al (2019).
The functions to download EDGAR are commented. This means that you must download all NetCDF files before running chem_edgar.

library(eixport)
library(rasterVis)
#> Loading required package: raster
#> Loading required package: sp
#> Loading required package: lattice
#> Loading required package: latticeExtra
library(cptcity)
# 1 Get EDGAR ####
# get_edgar(dataset = "v432_VOC_spec",
#           destpath = "V50_432_AP/TOT/",
#           sector = c( "TOTALS"),
#           type = "nc",
#           year = 2012)
#
# get_edgar(dataset = "v50_AP",
#           destpath = "V50_432_AP/TOT",
#           sector = c( "TOTALS"),
#           type = "nc",
#           year = 2014)
#
# get_edgar(dataset = "v432_VOC_spec",
#           destpath = "V50_432_AP/TRO/",
#           sector = c( "TRO"),
#           type = "nc",
#           year = 2012, ask = F)
#
# get_edgar(dataset = "v50_AP",
#           destpath = "V50_432_AP/TRO",
#           sector = c( "TRO_RES", "TRO_noRES"),
#           type = "nc",
#           year = 2014)
#
# totals <- list.files(path = "V50_432_AP/TOT/",
#                      full.names = TRUE,
#                      pattern = ".zip")
# lapply(totals, unzip, exdir = "V50_432_AP/TOT//")
#
#
# tros <- list.files(path = "V50_432_AP/TRO",
#                      full.names = TRUE,
#                      pattern = ".zip")
# lapply(tros, unzip, exdir = "V50_432_AP/TRO/")
a <- chem_edgar(path = "EDGAR/V50_432_AP/TOT", chem = "cbmz_mosaic")
#> units:  kg m-2 s-1
#> Detecting the following files:
#>                                                           lncs
#> voc1   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc1_2012.0.1x0.1.nc
#> voc2   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc2_2012.0.1x0.1.nc
#> voc3   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc3_2012.0.1x0.1.nc
#> voc4   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc4_2012.0.1x0.1.nc
#> voc5   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc5_2012.0.1x0.1.nc
#> voc6   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc6_2012.0.1x0.1.nc
#> voc7   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc7_2012.0.1x0.1.nc
#> voc8   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc8_2012.0.1x0.1.nc
#> voc9   EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc9_2012.0.1x0.1.nc
#> voc10 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc10_2012.0.1x0.1.nc
#> voc11 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc11_2012.0.1x0.1.nc
#> voc12 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc12_2012.0.1x0.1.nc
#> voc13 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc13_2012.0.1x0.1.nc
#> voc14 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc14_2012.0.1x0.1.nc
#> voc15 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc15_2012.0.1x0.1.nc
#> voc16 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc16_2012.0.1x0.1.nc
#> voc17 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc17_2012.0.1x0.1.nc
#> voc18 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc18_2012.0.1x0.1.nc
#> voc19 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc19_2012.0.1x0.1.nc
#> voc20 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc20_2012.0.1x0.1.nc
#> vpc21 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc21_2012.0.1x0.1.nc
#> voc22 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc22_2012.0.1x0.1.nc
#> voc23 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc23_2012.0.1x0.1.nc
#> voc24 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc24_2012.0.1x0.1.nc
#> voc25 EDGAR/V50_432_AP/TOT/v432_VOC_spec_voc25_2012.0.1x0.1.nc
#> co                 EDGAR/V50_432_AP/TOT/v50_CO_2014.0.1x0.1.nc
#> nox               EDGAR/V50_432_AP/TOT/v50_NOx_2014.0.1x0.1.nc
#> nmvoc           EDGAR/V50_432_AP/TOT/v50_NMVOC_2014.0.1x0.1.nc
#> so2               EDGAR/V50_432_AP/TOT/v50_SO2_2014.0.1x0.1.nc
#> nh3               EDGAR/V50_432_AP/TOT/v50_NH3_2014.0.1x0.1.nc
#> pm10             EDGAR/V50_432_AP/TOT/v50_PM10_2014.0.1x0.1.nc
#> pm25            EDGAR/V50_432_AP/TOT/v50_PM2.5_2014.0.1x0.1.nc
#> bc                 EDGAR/V50_432_AP/TOT/v50_BC_2014.0.1x0.1.nc
#> oc                 EDGAR/V50_432_AP/TOT/v50_OC_2014.0.1x0.1.nc
#> EDGAR data: voc1  voc2  voc3  voc4  voc5  voc6  voc7  voc8  voc9  voc10  voc11  voc12  voc13  voc14  voc15  voc16  voc17  voc18  voc19  voc20  voc21  voc22  voc23  voc24  voc25  co  nox  nmvoc  so2  nh3  pm10  pm2.5  bc  oc  units of gases: mol/km2/h
#> units of aerosols: ug/m2/s
# just for the plot, replaces 0 with NA

a$E_CO[] <- ifelse(a$E_CO[] <= 0, NA, a$E_CO[])
colors <- c(cpt(pal = "mpl_viridis", rev = F, n = 200),
            cpt(n = 100, rev = T))
levelplot(a$E_CO,
          col.regions = colors,
          zscaleLog = T)

Created on 2020-10-15 by the reprex package (v0.3.0)
Carter, W. P. (2015). Development of a database for chemical mechanism assignments for volatile organic emissions. Journal of the Air & Waste Management Association, 65(10), 1171-1184.
Lopez-Norena, Ana and Fernandez, Rafael & Puliafito, SALVADOR. (2019). ESPECIACION DE INVENTARIOS DE EMISIONES DE AEROSOLES Y COMPUESTOS ORGANICOS VOLATILES PARA EL MODELO WRF-CHEM, APLICADO A LOS ESQUEMAS RADM-2, CBM-Z Y MOZART
Ibarra-Espinosa et al., (2018). eixport: An R package to export emissions to atmospheric models. Journal of Open Source Software, 3(24), 607, https://doi.org/10.21105/joss.00607