How is it possible that a Hurricane such as Alex forms in the Atlantic in winter? Hurricane Alex (developed from an area of low pressure which began in the Atlantic near Bahamas in January 2016, and developed subtropical and later tropical storm characteristics to the south of the Azores) clearly way outside the usual Jun-Nov hurricane season. How is it possible that such an energetic event forms during the North Atlantic winter? Clearly, this kind of hurricane is not common (previous one in 1938). What are the oceanic and atmospheric conditions necessary for the formation of Hurricane Alex?

Alex was an out of season freak event that bucked the trend and broke all the rules. As hurricanes go, it was somewhat weak, and could never have strengthened beyond category 1. Three conditions made it happen (i) there was an unusually strong vortex over Cuba which kick-started the rotation, (ii) the high level atmospheric shear, which usually kills off fledgling hurricanes, just didn't appear on this occasion, (iii) the storm initially followed the Gulf Stream Drift, and hence gained energy from higher than usual sea surface temperatures - though still a lot cooler than normal. After the hurricane reached central north Atlantic it veered off to the northwest, where it met much colder sea-surface temperatures. It was this cold water that killed it off.


Why do felsic materials have lower melting points than mafic? It is clear from Bowen's reaction series that more felsic minerals have lower melting points than mafic minerals. As far as I know, the same is true of quenched glasses.

Felsics have a higher degree of SiO2 polymerization in the solid phase, which I would have thought was energetically favorable, and therefore I would have expected a felsic glass to require more energy to melt than a corresponding basaltic glass and therefore have a higher melting temperature. However, the opposite is true. Why is this the case?

Good question! As you know, Bowen's reaction series describes the order of crystallization of silicate minerals in a cooling magma.

The complex anion of silicates is a tetrahedron of four oxygen atoms surrounding one silicon atom, connected with strong covalent bonds. Each tetrahedron may be isolated from one another or they may be bonded together covalently by sharing oxygen atoms between adjacent tetrahedra. In this way they may form single chains (pyroxene), double chains (amphibole), sheets (biotite), and three-dimensional networks of interlocking tetrahedra (quartz).

Each of these covalently bonded structural groups (except 3D networks) is bonded to its neighboring structural group (e.g. single chain to single chain) by ionic bonds with intervening cations (K+, Na+, Ca2+, Mg2+, Fe2+, etc.).

Relatively speaking, covalent bonds have lower melting points than ionic bonds. Source

In Bowen's reaction series, the minerals that form at the cooler end of the discontinuous series are richer in silicon and oxygen and poorer in metal cations. Therefore, the minerals at the cooler end are also more dominated by covalent bonds over ionic bonds. This prevalence is the reason why felsic minerals melt at lower temperatures than mafic ones.

Your logic is correct when looking at mineral stability in the face of chemical weathering. At the Earth's surface, those covalent bonds are much more stable and minerals like quartz tend to be much more resistant to weathering than olivine or pyroxene. This is described in the Goldich stability series, which I like to think of as Bowen's reaction series stood on its head.


What would happen if I installed distributed water fountains in all of the ocean's dead zones? As every thalassologist knows, the ocean is filled with great streches of desert with no marine photosynthetic life. What would happen if, for each of these oceanic deserts, I densely filled them with solar and wind "oil rigs" with water fountains surrounding them that pump up volumes of deep oceanic water and jets them out of the water? Let's suppose these solar and wind rigs operated continually.

That sounds like a method to attempt to increase the global humidity level.  I'm not sure how many areas we're talking, or who large they are, but "all of" just sounds like a lot.  It's at least going to be a massive humidifier the way you've designed it.
I would think if you want to achieve your goal without radically humidifying the air, it would probably work better to design your apparatus after aquarium aerators.
Somebody is studying this idea in the Baltic Sea. (thank you trond hansen).

A main threat for the Baltic is at first the human induced pollution through industries, agriculture, deforestation, private homes, shipping and tourism. Waste or erosion water due to these practices, contains lots of N and P that stimulate primary production. Also rising sea water temperatures due to global climate change make the oxygen availability even worse, since less O2 can be dissolved in warmer water (USGS, 2016). Moreover, also pharmaceutics or other artificial chemical substances that end up in waste water contribute for a great deal to the dead zones. Not directly by inducing primary production, but maybe even worse; the toxins they contain can either directly kill marine life or add up in the food chain and cause health troubles. Also reproductive success can be affected by certain chemicals.
In an attempt to stop the eutrophication and bring back life into dead zones, Swedish scientist came up with the idea of engineered re-oxygenation of the Baltic’s ‘dead zones’. Pumping relatively oxygen rich water from the surface into deeper anoxic layers, hereby breaking through the oxycline and allowing the water to mix again, must safe the Baltic according to the BOX project (Forth, 2015).
Facilitating nature to restore itself, instead of only focussing on banning the resources, could accelerate the battle against eutrophication in the Baltic. However, many scientists are sceptical about the idea. Prof. Conley (Lund University) mentions that there might be unforeseen consequences that could change the whole ecosystem. He also mentions that oxygenation could affect the reproductive success of some fish species. That’s indeed true, but Koster et al. (2005) found that increased O2 – levels actually improve the reproductive volume of at least cod, which thus makes it a benefit.
Apart from the costs of the BOX project, another point for consideration is the fact that treating the consequences instead of the sources might cause ignorance to obligations that try to reduce waste dumping, since it will be cleaned anyway.

In areas that are devoid of life due to a eutropic cycle (excessive nutrients in the water triggered a population explosion, which consumed all available oxygen, so everything suffocated), it would probably be helpful, so long as the aeration reaches the perimeter of the area.
However, if other pollution is involved, that changes things significantly.  Even if the initial death was due to eutrophication, developing life can be much more susceptible to toxic chemicals than mature life.  Because of this and other factors, what works for one part of the ocean may not work in other parts.
It's also worth noting that dead zones are not confined just to the oceans.  Something like this is being done in small lakes in Canada (Thank you Friddy).

Ecologically, many of these nutrient rich small lakes are classified as being in a eutrophic state. These lakes are typically shallow, with maximum depths of less than 50 ft and average depths of less than 15 ft. They are often landlocked or have only intermittent or seasonal stream connections. Because they are rich in nutrients, they are abundant in aquatic plants and invertebrates. Eutrophic lakes can be extremely productive and have the capability to grow trout and char very quickly and to large sizes. However, the same features that make accelerated growth possible in the summer can make it difficult for trout to survive over winter. Some of our most popular interior trout lakes are marginal in terms of their ability to support trout through the colder months due to a drop in dissolved oxygen levels Thousands of small trout lakes in BC’s interior freeze over for 4 to 6 months each winter. During that ice-covered period, the aquatic plants that grew through the open water seasons die off from lack of sunlight. Dissolved oxygen in the water is used up as this plant matter decomposes. As winter progresses, the volume of oxygenated water decreases to levels that become too low to support trout life, which results in a winterkill.
There are also small lakes located in the coastal regions that suffer from severe oxygen depletion during the summer months. This affects the ability of trout to survive as well as having impacts on the quality of the water for domestic use and other forms of aquatic recreation.

Their situation is quite different.  Rather than trying to resolve issues with pollution, they're using it to allow fish in ice-covered ponds to survive the winter.  There's also some lakes with issues during the summer the article doesn't go into detail about, which could be pollution or it could be natural eutrophication.  For the winter use case, it apparently works at two levels, as not only does it provide direct oxygenation of the water, but it also adds resistance to icing over directly above where the aeration is done, providing some surface that continues to be able to get oxygen from the air.
Both linked articles explicitly state this is just one tool to solve one particular issue, not a solution for all pollution, fish survival, and water quality issues.


Lava and Magma, Meteor and Meteorite--What's the Difference? Some features of Earth (and beyond) have been bugging me for quite some time.  When molten rock is beneath the surface, it's called "magma".  Above the surface, it is called "lava".  When a bolide is in orbit of any planet, it's called a "meteoroid".  When it makes contact with the atmosphere, it becomes a "meteor".  When it hits the surface, it becomes a "meteorite".  But changing one's name based on a change in location is, at best, superficial and has no relation to chemistry and composition.

Are there any REAL differences between magma and lava?  Are there any REAL differences between a meteoroid, a meteor and a meteorite?  If not, then why do we call them by different names?

There is a real difference between lava and magma. Magma contains lots of water and various gases. When it reaches earth's surface, it looses most of its volatiles and becomes lava.

So, magma is volatile rich lava.


Correct way to spin-up for sensitivity analysis in a Global Chemical Transport Models In my understanding, in Global Chemical Transport Models,  initial conditions are provided by spinning up the model or through previous simulation.  But, can I use the same initial condition that I prepared for baseline simulation for sensitivity analysis too?
For example:
I am trying to  see the changes in the surface ozone for the year 2010. I conducted baseline simulation from beginning of 2009  till the end of 2010 with 2009 taken as spin-up period.
Then, to see the changes from anthropogenic emission, I turned off anthropogenic emission sources and would like to simulate it for the year 2010. In this case can I use the initial condition values that I prepared from the baseline simulation or do I have to spin-up from the beginning?

welcome to ES.
I'm not experienced in Chemical Transport Model, so my reply will be more on "general terms" and I'd be more than happy to see more specific answers.
That being told, in my experience with land surface and hydrological models, the spin-up period is heavily model- and application- specific. In some applications, for hydrological analysis of complex large basins, the spin up period of the model could require also some years of simulation. This is needed to stabilize all the "reservoirs" that the model uses to simulate the complex interactions between surface and subsurface hydrology. Using a "too short" spin-up period would seriously affect your results, but what is "too short" depends on your model.
The sort answer to your question would therefore be: "the longer the better", but this would also depend on the complexity of your model and the computing time that you need to dedicate to the spin-up period (i.e., if your model takes a week to simulate a month, then I would not suggest you to take a spin-up period of a year...).
So if this is not the case, and your model is pretty fast, I'd replicate the data that you prepared for the year 2009 as input for a "fake" 2008 and run the simulation from 2008 to 2010. Looking at the outputs of your model, then, you will see if there are weird trends that you could attribute to the spin-up going beyond the "fake" 2008. If this is not enough, you could use the same data also for 2007, and so on.
Additional info also here.
EDIT: I'm just realizing that yesterday I replied only to part of your question. My apologies.
I'll try to respond here to the question you made in your comment:

So that means even if I take the initial condition form year 2009, I
still need to spin up the model, if I do some other analysis (such as
sensitivity)? can't I directly use the initial condition from 2009
from baseline simulation in sensitivity simulation instead of spinning
up again, especially if the spin up time is very long and I would want
to save computation time?

This really depends on the inertia of the system that you are analyzing. It is very unlikely (at least, to me, and remember that I'm not familiar with these models) that you could get a system that is going to stabilize to different conditions (like removing the anthropogenic emissions) simulating just one year.
Look for instance at this example that comes from the User Guide of an open access hydrological model (LISFLOOD documentation, taken from here):

To better understand the impact of the initial model state on the
results of a simulation, let’s start with a simple example. The Figure
below shows 3 LISFLOOD simulations of soil moisture for the upper soil
layer. In the first simulation, it was assumed that the soil is
initially completely saturated. In the second one, the soil was
assumed to be completely dry (i.e. at residual moisture content).
Finally, a third simulation was done where the initial soil moisture
content was assumed to be in between these two extremes.

So, this part of the system (top layer soil moisture), is apparently stabilizing in about 150-200 days. After this period, the initial conditions (with and without spin-up) are basically irrelevant.
If you go to analyze other variables instead:

This part of the system, instead, takes about 2000 days to stabilize. This means that a proper initialization of this particular model in this particular application is crucial to get reliable results in "short" (<2000 days in this case) simulations.
Now, imagine that your system has a certain inertia to stabilize after a shock in the emission level (from the example you made in your question): if your situation is similar to the one in the first figure, one year spin-up would be enough and the comparison of the second year with and without anthropogenic emissions would give you something more or less informative. If your system has a slow response as the one in the second figure, then your results would be completely wrong even with the spin-up that you proposed.


Publicly-available real-time world-wide seismographic information? Is there a list of websites supplying real time seismographic information for around the world?

I could find for specific places. But I'm looking for a list so I can check for anywhere (providing, of course, they have it.)


How to generate inputs for the MUNICH model from the VEIN model MUNICH is the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) (Kim et al., 2018) and VEIN is the Vehicular Emissions INventory model (Ibarra-Espinosa et al, 2018), an R package.
VEIN estimates vehicular emissions at street level, generating useful inputs for the MUNICH model, however, some pre-processing is necessary. Therefore, the model eixport was created to help in this processing.
As a maintainer I receive many emails asking how to generate inputs for the MUNICH model

Kim, Y., Wu, Y., Seigneur, C., and Roustan, Y.: Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1), Geosci. Model Dev., 11, 611-629, https://doi.org/10.5194/gmd-11-611-2018, 2018.
Ibarra-Espinosa, S., Ynoue, R., O'Sullivan, S., Pebesma, E., Andrade, M. D. F., and Osses, M.: VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories, Geosci. Model Dev., 11, 2209–2229, https://doi.org/10.5194/gmd-11-2209-2018, 2018.
Ibarra-Espinosa et al., (2018b). 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

First, download a project with vein and estimate vehicular emissions.
Some projects are here: https://atmoschem.github.io/vein/reference/get_project.html
library(vein)
get_project(directory = "awesomecity")

Then open the file main.Rproj with Rstudio and source the file main.R
Then, run the following code

library(data.table)
library(eixport)
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(writexl)
library(vein)
library(units)
#> udunits system database from /usr/share/xml/udunits
# epsg 31983, projected UTM
co <- readRDS("post/streets/CO.rds")
st_crs(co)
#> Coordinate Reference System:
#>   User input: EPSG:31983
#>   wkt:
#> PROJCS["SIRGAS 2000 / UTM zone 23S",
#>     GEOGCS["SIRGAS 2000",
#>         DATUM["Sistema_de_Referencia_Geocentrico_para_las_AmericaS_2000",
#>             SPHEROID["GRS 1980",6378137,298.257222101,
#>                 AUTHORITY["EPSG","7019"]],
#>             TOWGS84[0,0,0,0,0,0,0],
#>             AUTHORITY["EPSG","6674"]],
#>         PRIMEM["Greenwich",0,
#>             AUTHORITY["EPSG","8901"]],
#>         UNIT["degree",0.0174532925199433,
#>             AUTHORITY["EPSG","9122"]],
#>         AUTHORITY["EPSG","4674"]],
#>     PROJECTION["Transverse_Mercator"],
#>     PARAMETER["latitude_of_origin",0],
#>     PARAMETER["central_meridian",-45],
#>     PARAMETER["scale_factor",0.9996],
#>     PARAMETER["false_easting",500000],
#>     PARAMETER["false_northing",10000000],
#>     UNIT["metre",1,
#>         AUTHORITY["EPSG","9001"]],
#>     AXIS["Easting",EAST],
#>     AXIS["Northing",NORTH],
#>     AUTHORITY["EPSG","31983"]]
head(co, 1) # units g/h
#> Simple feature collection with 1 feature and 25 fields
#> geometry type:  LINESTRING
#> dimension:      XY
#> bbox:           xmin: 328001.4 ymin: 7391129 xmax: 328036.5 ymax: 7391204
#> projected CRS:  SIRGAS 2000 / UTM zone 23S
#>        id            V1             V2             V3             V4
#> 1 1 [1/h] 275.088 [g/h] 140.0241 [g/h] 84.63286 [g/h] 86.81989 [g/h]
#>               V5             V6             V7         V8             V9
#> 1 195.8425 [g/h] 680.7458 [g/h] 1423.767 [g/h] 1719 [g/h] 1723.265 [g/h]
#>              V10            V11            V12            V13          V14
#> 1 1748.996 [g/h] 1600.077 [g/h] 1448.586 [g/h] 1378.825 [g/h] 1551.5 [g/h]
#>              V15            V16            V17            V18            V19
#> 1 1556.648 [g/h] 1538.186 [g/h] 1698.133 [g/h] 1655.548 [g/h] 1601.138 [g/h]
#>              V20            V21            V22            V23            V24
#> 1 1398.703 [g/h] 941.4398 [g/h] 709.3241 [g/h] 581.2601 [g/h] 407.1127 [g/h]
#>                         geometry
#> 1 LINESTRING (328001.4 739120...

# example of  polygon as buffer at center
st_bbox(co) %>%
  st_as_sfc() %>%
  st_centroid() %>%
  st_buffer(dist = 2000) -> polygon

# if you have another polygon, read it
# polygon <- st_read("/path/to/polygon.gpkg")

cob <- st_intersection(co, polygon)
#> Warning: attribute variables are assumed to be spatially constant throughout all
#> geometries

# Remove ID
co$id <- NULL

# split on vertex conserving mass
cos <- st_explode(cob)
#> Sum: 16542955.31
dim(cos)
#> [1] 372  27
# it adds length LKM and LKM2
# Should we remove streets shorter than 10 mts?
cos <- cos[as.numeric(cos$LKM2) > 10, ]

# remove LKM and LKM2
cos$LKM <- cos$LKM2 <- NULL

# We need to add units back, g/h
cos <- vein::Emissions(cos, time = "1/h")
head(cos, 1)
#> Simple feature collection with 1 feature and 24 fields
#> geometry type:  LINESTRING
#> dimension:      XY
#> bbox:           xmin: 331916.3 ymin: 7385767 xmax: 331922.9 ymax: 7385775
#> projected CRS:  SIRGAS 2000 / UTM zone 23S
#>               V1             V2             V3             V4             V5
#> 1 59.38562 [g/h] 30.22821 [g/h] 18.27042 [g/h] 18.74256 [g/h] 42.27821 [g/h]
#>               V6             V7             V8             V9            V10
#> 1 146.9585 [g/h] 307.3609 [g/h] 371.0953 [g/h] 372.0159 [g/h] 377.5708 [g/h]
#>              V11            V12            V13            V14            V15
#> 1 345.4224 [g/h] 312.7188 [g/h] 297.6588 [g/h] 334.9356 [g/h] 336.0469 [g/h]
#>              V16            V17            V18            V19          V20
#> 1 332.0614 [g/h] 366.5907 [g/h] 357.3973 [g/h] 345.6514 [g/h] 301.95 [g/h]
#>              V21            V22            V23            V24
#> 1 203.2367 [g/h] 153.1279 [g/h] 125.4816 [g/h] 87.88691 [g/h]
#>                         geometry
#> 1 LINESTRING (331916.3 738577...
# now we transform for the REQUIRED UNITS FOR MUNICH
# ug/km/h

# we have 24 hours in this case
hours <- paste0("V", 1:24)

for (i in seq_along(hours)) {
  cos[[hours[i]]] <- set_units(cos[[hours[i]]], ug / h)
  cos[[hours[i]]] <- cos[[hours[i]]] / set_units(st_length(cos), km)
}
plot(cos["V1"], axes = T)

etm <- to_munich(sdf = cos)
names(etm)
#> [1] "Emissions" "Street"
head(etm$Emissions, 1)
#>   i idbrin typo        xa        ya        xb        yb                   V1
#> 1 1      1    0 -46.64773 -23.62993 -46.64766 -23.63001 5462199660 [ug/h/km]
#>                     V2                   V3                   V4
#> 1 2780345076 [ug/h/km] 1680485867 [ug/h/km] 1723911918 [ug/h/km]
#>                     V5                    V6                    V7
#> 1 3888685780 [ug/h/km] 13517016236 [ug/h/km] 28270586844 [ug/h/km]
#>                      V8                    V9                   V10
#> 1 34132780053 [ug/h/km] 34217462106 [ug/h/km] 34728386913 [ug/h/km]
#>                     V11                   V12                   V13
#> 1 31771425772 [ug/h/km] 28763397272 [ug/h/km] 27378203813 [ug/h/km]
#>                     V14                   V15                   V16
#> 1 30806871891 [ug/h/km] 30909088139 [ug/h/km] 30542507720 [ug/h/km]
#>                     V17                   V18                   V19
#> 1 33718452534 [ug/h/km] 32872865695 [ug/h/km] 31792493506 [ug/h/km]
#>                     V20                   V21                   V22
#> 1 27772900557 [ug/h/km] 18693404826 [ug/h/km] 14084471885 [ug/h/km]
#>                     V23                  V24
#> 1 11541609851 [ug/h/km] 8083705065 [ug/h/km]
head(etm$Street, 1)
#>   i       length width height
#> 1 1 10.87211 [m]     0     30

# to export to CSV which supports bit64
data.table::fwrite(
  etm$Emissions,
  paste0(basename(getwd()), "_Emissions.csv")
)
data.table::fwrite(
  etm$Street,
  paste0(basename(getwd()), "_Streets.csv")
)

# to export to Excel
writexl::write_xlsx(
  x = etm$Emissions,
  path = paste0(basename(getwd()), "_Emissions.xlsx")
)
writexl::write_xlsx(
  x = etm$Street,
  path = paste0(basename(getwd()), "_Streets.xlsx")
)

Created on 2020-10-14 by the reprex package (v0.3.0)


What controls the tendency of trace elements in different particle size fractions? Trace element atmospheric particles are introduced from different sources, with both anthropogenic and natural origins.

By analyzing the concentration levels of 18 elements in samples of $PM_{2.5}$ and $PM_{10}$, the results showed that the size distributions of those elements are significantly different.

A number of papers explained that difference in size fraction of various species is related to their different sources.

  Natural origin: the crustal element (i.e., $Al$, $Ca$, $Mg$) are always accumulated in coarser particles.
  Anthropogenic origin: the anthropogenic emissions ($Cu$, $Zn$, $As$, $Pb$) are always accumulated in finer particles.

I'm wondering if the chemical property of different species are important in determining their size distribution as well.

In other words, is size distribution not solely related to emission source (natural abrasion => coarse particles; anthropogenic combustion => fine particles)?

If so, what chemical properties would be essential to infer the tendency of the specific elements (into coarse particles or fine particle).

Any advice would be appreciated!

PS

In previous research, I have found out one factor might be important:

Volatity

  "The volatile elements (e.g., $As, Cd, Tl, Cu, Pb, Zn$) are strongly correlated across the size distribution and exist in the plume primarily as fine sulfate (0.6 mm diameter) with lesser amounts transported as coarse sulfates (3.5 mm diameter) and coarse chlorides (11 mm diameter)." copied from $\text{Martin et al., JGR, 2012}$

Crustal elements are essentially from soil which have undergone compaction during geologic processes.  Particles are then lifted into the air (e.g. by wind) and coarse particles from the soil are suspended in the atmosphere.  If the particulate is from wildfire, it is still of "natural origin", but the particulate is from a combustion source.  Combustion creates fine particles.

Anthropogenic particulate emissions are dominated by combustion sources, because so much of our industry and energy production uses combustible fuels.  Combustion creates fine particles.  However, there are still several anthropogenic sources that create coarse particulate (e.g. road dust, construction dust, agricultural processes, etc.).


Why are methane spectroscopic parameters harder to determine and relatively poorly known, compared to other Earth trace gases? In atmospheric remote sensing of methane, a dominant source of error is the uncertainty in spectroscopic parameters.  For example, in the retrieval algorithms described by Batchelor et al. (2009), the CH₄ error due to line intensities is estimated at 16.5% (dominating the total error of 16.8%), versus 10.5% for O₃, 3.8% for N₂O, and only 1.6% for HCl.

The article documenting the 2012 HITRAN spectroscopic database update (Rothman et al., 2013) states:

  High-resolution spectroscopy of methane and the generation of accurate line lists, especially as one moves up the polyad ladder to shorter and shorter wavelength (necessary for many applications) is extremely challenging.

The 2012 edition includes a very large update on methane measurements, as detailed by Brown et al. (2013), who describe how the number of HITRAN methane lines has increased from 11,803 in the 1982 edition, via 251,440 in the 2004 edition, to 468,013 in the 2012 edition.  However, neither Rothman et al. (2013) nor Brown et al. (2013) describe why the determination of methane spectroscopic parameters is so difficult.

Why are methane spectroscopic parameters harder to determine (and therefore more poorly known) compared to other common Earth atmospheric trace gases?

Batchelor, Rebecca L., Kimberly Strong, Rodica Lindenmaier, Richard L. Mittermeier, Hans Fast, James R. Drummond, and Pierre F. Fogal. "A New Bruker IFS 125HR FTIR Spectrometer for the Polar Environment Atmospheric Research Laboratory at Eureka, Nunavut, Canada: Measurements and Comparison with the Existing Bomem DA8 Spectrometer." Journal of Atmospheric & Oceanic Technology 26, no. 7 (2009).
L.S. Rothman, I.E. Gordon, Y. Babikov, A. Barbe, D. Chris Benner, P.F. Bernath, M. Birk, L. Bizzocchi, V. Boudon, L.R. Brown, A. Campargue, K. Chance, E.A. Cohen, L.H. Coudert, V.M. Devi, B.J. Drouin, A. Fayt, J.-M. Flaud, R.R. Gamache, J.J. Harrison, J.-M. Hartmann, C. Hill, J.T. Hodges, D. Jacquemart, A. Jolly, J. Lamouroux, R.J. Le Roy, G. Li, D.A. Long, O.M. Lyulin, C.J. Mackie, S.T. Massie, S. Mikhailenko, H.S.P. Müller, O.V. Naumenko, A.V. Nikitin, J. Orphal, V. Perevalov, A. Perrin, E.R. Polovtseva, C. Richard, M.A.H. Smith, E. Starikova, K. Sung, S. Tashkun, J. Tennyson, G.C. Toon, Vl.G. Tyuterev, G. Wagner, The HITRAN2012 molecular spectroscopic database, Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 130, November 2013, Pages 4-50, ISSN 0022-4073, http://dx.doi.org/10.1016/j.jqsrt.2013.07.002.
(http://www.sciencedirect.com/science/article/pii/S0022407313002859)
L.R. Brown, K. Sung, D.C. Benner, V.M. Devi, V. Boudon, T. Gabard, C. Wenger, A. Campargue, O. Leshchishina, S. Kassi, D. Mondelain, L. Wang, L. Daumont, L. Régalia, M. Rey, X. Thomas, Vl. G. Tyuterev, O.M. Lyulin, A.V. Nikitin, H.M. Niederer, S. Albert, S. Bauerecker, M. Quack, J.J. O’Brien, I.E. Gordon, L.S. Rothman, H. Sasada, A. Coustenis, M.A.H. Smith, T. Carrington Jr., X.-G. Wang, A.W. Mantz, P.T. Spickler, Methane line parameters in the HITRAN2012 database, Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 130, November 2013, Pages 201-219, ISSN 0022-4073, http://dx.doi.org/10.1016/j.jqsrt.2013.06.020. (http://www.sciencedirect.com/science/article/pii/S0022407313002744)

A good explanation is found at the University of Bourgogne site MODELING THE ABSORPTION SPECTRUM OF METHANE.

  Although it is a small and simple molecule, its rovibrational spectroscopy is very complicated. This is mainly due to the high symmetry of this tetrahedral system (which leads to the existence of many degeneracies) and to its intricated vibrational structure.

Vibrational and Rotational-vibrational energy level diagrams are available at the site.


What is the origin of the Laacher See caldera magma in Germany? The Laacher See is a volcanic crater lake in Germany, with a diameter of 2 km (1.2 mi) in Rhineland-Palatinate area, approximately 24 km (15 mi) northwest of Koblenz and 37 km (23 mi) south of Bonn, and is closest to the town of Andernach situated 8 km (5.0 mi) to the east on the river Rhine.

According to the Volcano Discovery web page about the volcano, it last erupted about 12.9 thousand years ago, in an eruption larger in size than Mt. St. Helens 1980 eruption.  It is believed to have erupted in a Plinian style.  The website classifies the volcanic system as dormant, with emissions of $\ce{CO2}$ and seismicity recorded in the area.

However, the region of Germany that the Laacher See and the surrounding East Eifel mountains are not on a plate boundary.

What is the origin of the Laacher See caldera magma in Germany?

The East Eifel Volcanics, of which the Laacher See Caldera is a part of, is located on the Rhenish Shield and is associated with the Cenozoic Upper Rhine Graben continental rift zone (Aber, 2012).

Ukrich and Gabrielle (2013) hypothesise that as the region did have and still has active tectonics, that the magma chamber for the Laacher See Caldera could have been the result of magma accumulating in voids opened up through regional strike-slip faulting (including the 'Laacher See Strike Slip' and Ochtending Fault), block rotation and regional uplift.

According to Schmitt et al. (2010), the parental magmatic melt that formed the carbonatite-syenite suite based petrology that dominates the Laacher See caldera is derived from a mantle melt differentiated to phonolite.

It is believed that the magma resided/accumulated for around 20,000 years prior to the eruption.  Schmitt et al. hypothesise that the magma had a cooler and older outer edge, compositionally zoning to a hotter phonolitic core - the presence of younger phenocrysts could indicate a magma reorganisation just prior to the eruption.

References

Aber, 2012, Volcanism of the Eifel, Germany Region, GO 326 Plate Tectonics course notes

Schmitt et al. 2010, Magmatic Longevity of Laacher See Volcano (Eifel, Germany) Indicated by U-Th Dating of Intrusive Carbonatites, Journal of Petrology

Ulrich and Gabrielle, 2013, Why does the Size of the Laacher See Magma Chamber and its Caldera Size not go together? - New Findings with regard to Active Tectonics in the East Eifel Volcanic Field, EGU General Assembly 2013


How much distance does $0.001^\circ$ in latitude or longitude represent? If I'm standing at a lat of 30.000$^\circ$ and a long of 30.000$^\circ$ and I move to a lat of 30.001$^\circ$ and a long 30.000$^\circ$, how far have I gone? Is there a relationship between decimal places and lat and long distances?

I don't understand the last sentence about decimal places, but I can tell you about the relationship between lat, long and distance.

Over two centuries ago, the meter was defined as one ten-millionth (1/10 000 000) of the length of a quadrant along the Earth's meridian; that is, the distance from the Equator to the North Pole. So, for latitude the number of degrees from the pole to the equator is $90^\circ\!$, and the number of meters is 10 million (or 10,000 kilometers). That means $1^\circ\!$ of latitude is $10,000/90 = 111$ kilometers, and $0.001^\circ = 0.111$ kilometers or $111$ meters, essentially an American football field plus both endzones.

The total length of the Equator is about equal to four times the distance from a Pole to the Equator. (Slightly more because the Earth's figure is a little oblate, like a slightly flattened ball.) Think of a great circle going from the North pole, to the Equator, to the South pole, to the Equator on the opposite side of the world, then back to North Pole ($360^\circ\!\!$ of a circle). That would be (almost) equivalent to the great circle ($360^\circ\!\!$ of longitude) of the Equator. So, at the Equator, $0.001^\circ\!$ of longitude will also be $111$ meters. But...

The other circles of latitude are smaller than the great circle of the Equator, yet still have $360^\circ\!$, so those degrees cover less than $111$ kilometers.

In fact, the size of a degree of longitude as a function of latitude scales as the cosine of the latitude. So $0.001^\circ\!$ of longitude change at $30^\circ\!$ latitude would be $111 \times \cos (30^\circ\!) = 111 \times 0.866 = 96$ meters.


Why is Earth's Core Iron? The two major constituents of the Earth's core are iron and nickel.  In documentaries and scientific conversations, iron gets more attention than nickel, probably because it makes up a bigger percentage of the core.

But before I consider this scenario—where it was nickel that makes up the bigger percentage instead of iron—I must ask:

Why does iron make up the majority of Earth's core as opposed to nickel?  Does either metal have an advantage over the other?  If so, which advantage?  If nickel is the number-one metal of the core instead of iron, how would the magnetic field be affected?

Simply put, the amount of iron in the solar system/universe is higher than nickel due to a process called nucleosynthesis.

Basically at it's origin the universe comprised only of hydrogen, through nuclear fusion heavier elements were/are being created. Fundamentally, hydrogen is fused into heavier elements in the star, our sun is currently turning massive amounts of hydrogen into helium through the proton-proton chain.

Eventually as a star gets older you start seeing fusion of heavier elements, helium, carbon, nitrogen, etc up until you hit iron at which point you hit what is known as the iron peak  where fusing iron atoms together requires more energy than is release. Heavier elements are all formed during supernovas and thus we see lower level of abundance. That being said the abundance of nickel is still higher than all the other heavier metals.

https://en.wikipedia.org/wiki/Stellar_nucleosynthesis

http://physics.info/nucleosynthesis/


Can climate models reliably predict meteorological extremes? This question is related to and extension of my previous question which was about the methodology used to predict future climatic extremes:

What is the methodology to analyze future climatic extremes using the results of climate models?

Although the answer to that question is quite detailed and informative, it is still not clear to me whether the models used in global climate (GC) simulations can predict rare (~1000-year return period events).

I understand that if we have multi-thousand simulations, we can
calculate the 0.1% quantile, but I do not know whether the applied
models can predict rare events reliably. Under reliably, I mean that
in some alignment with the observed extremes or with the extremes
statistically inferred from the observations.
All the papers I saw so far, which dealing with extremes due to
climate change, are considering 20-year return period events as
maximum and comparing the simulations to observed quantiles. This is
one source of my suspicion regarding the reliability of GCMs.
Are GCMs calibrated to observed mean or multiple quantiles? Daily,
monthly, annual, etc. averages are used? Averages of larger areas or
grid points (corresponding to the applied resolutions) are adopted in
the calibration, validation processes? What are the typically used
target parameters used in the calibrations?
Here is an example to make it more clear: one wants to determine the
probability distribution function of extreme wind speed in a given
location from daily measurements/simulation results. The question: If we take one
simulation for the 1950-2000 period and assume for a moment that
model uncertainty is negligible compared to climate variability, can
we treat the simulation data as a sample with more or less equivalent
confidence than that of the actual observations, corresponding to the
same period, to infer large return period extremes?

One way to check this is would be to compare the large return period extremes inferred from observed data and from multiple simulations, I have not seen any papers with above 20-year return level comparison yet.
I see that we cannot expect very accurate models regarding e.g. 1000-year extremes, because we cannot reliably calibrate and validate GCMs:

The available measurements are from a 100-200 year observation period (at best);
We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.

Any help, idea, reference, link are welcomed.

Here's one idea to account for meteorological extremes in our climate models, though I'm not sure if it answers your question and am less certain if it qualifies as reliable (open for feedback on that too):

Take a regional climate model which has simulated into the past and the future. We also have a few decades of observed data to compare the same time period of simulated past. The observed data is going to capture the extreme events much better in every way of course. So, in projecting into the future, if we want to maintain those extremes, we can use the change factor (based on averages, e.g. monthly means) from past-simulation==>future-simulation in order to project past-observed==>future-predicted. Those future-predictions will have the extreme events as they came in the past but with the changed magnitude based on the climate model's past versus future simulations.

This could be applied to meteorological data such as precipitation and other weather variables in order to inform us about the driving weather forces we'll face in future scenarios. In this way our climate models account for meteorological extremes we've observed recently and in the past, which the models may otherwise fall short on.

Otherwise, as folks have explained in comments on the OP, we simply don't have extensive enough observed data that we can rely upon to calibrate these rarer frequency events. I think your last two bullet points sum up our predicament fairly well:

  The available measurements are from a 100-200 year observation period (at best);
  We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.

However, here are a few studies and leads you could explore which try to overcome these challenges in modeling extreme event return intervals and Probable Maximum Precipitation. These seem like they might be addressing your last question but hopefully it helps clarify the potential out there.

This one using extreme-value theory: Van Den Brink et al, 2004. Estimating return periods of extreme events from ECMWF seasonal forecast ensembles.

  ...applications to meteorological and hydrological situations are
  always  hampered  by  the  brevity  of  the  available  datasets,
  as  the  required  return  levels  exceed  the  record  lengths  by  a
  factor of 10 to 100. In order to overcome this problem, we u
  se archived data from all past seasonal forecast ensemble
  runs  of  the  European  Centre  for  Medium-Range  Weather  F
  orecasts  (ECMWF)  since  1987  as  input  for  extreme-value
  statistics analysis. We make use of the fact that the seasonal forecast has little seasonal skill for the Netherlands, which
  implies that the ensembles can be regarded as i
  ndependent sets that cumulate to over 1500 years.
  We investigate the hydraulic response in the Netherlands
  to extreme synoptic-scale weather systems by studying the
  extreme-value  distributions  of  sea  storm  surge  levels,  wa
  ves  and  river  discharges.  The  application  is  detailed  in  four
  practical examples originating from coastal protection, ri
  ver flooding protection, and water management problems. The
  long record length of the ECMWF data reduces the uncertainty in the 10
  3
  -year and the 10
  4
  -year return values considerably
  with respect to the results based on observational time ser
  ies. The ECMWF dataset gives the opportunity to explore the
  distribution of events that depend on several kinds of extreme (Van Den Brink et al, 2004).

This one using the growth factor method, or "NERC M5 method", as explained on page 10: Skaugen and Førland, 2011. Future change in extreme precipitation estimated in Norwegian catchments.

And here is a study which sums the methods up pretty well and proposes a new one for further research, which sounds good to me and relatively like what I described in pretty broad, simple terms in the first part of my answer: Dyrrdal, 2012. Estimation of extreme precipitation in Norway and a summary of the state-of-the-art. There's many quote-worthy passages in this one so I think I'll leave it to you to explore. Sections 3, 4, and 5 are what I imagine you're most interested in because they cover, respectively, the state-of-the-art guidelines/methods from the World Meteorological Organization and various nations; a proposed new-and-improved method; and a conclusion summarize the above.


Why should dynamical downscaling of meteorological data be made incrementally? When downscaling meteorological data by using limited area models, many support forums recommended you to downscale incrementally in small steps. For example let's assume that your input data is on a 27x27 km resolution and you need data on 1x1 km resolution.

A suitable setup would be to downscale in three steps, incrementally increasing the resolution:
27 km -> 9 km -> 3 km -> 1 km

Why should I not simply use the following set-up?
27 km -> 1 km

The main motivations I've seen for this is that the intermediate domains (9 & 3 in the example) won't require that much computational resources compared to the final domain (1 km). But that can't be the only reason.

The basic answer to your question is that there needs to be an overlap between the spectra of the two domains. One way to think about it is that there needs to be a connection between the Kolmogorov energy cascades (https://en.wikipedia.org/wiki/Energy_cascade) of the two domains.

Kolmogorov energy cascade (D. Newman, U. Alaska Fairbanks).

Each domain is going to be forced (have energy input) at a relatively large scale and it is going to assume that the energy that it can't resolve at sub-grid scales is going to be dissipated. If there is no (or little) overlap between the two grid resolutions, the energy from the large domain (parent) will be dissipated by viscosity at sub-grid scales that the small domain (child) cannot resolve and there is not going to be an appropriate input of energy from the parent to the child.

The reason for the odd numbers in the ratio is given in Why is WRF most often configured at 3:1 nesting ratio?


Extraterrestrial Planetary Sky Color: does it have to be blue? Earth's sky appears blue, due to the composition of the atmosphere and refraction.  Mars' sky is reddish, due to the high iron-oxide particle content of the air.

If a planet had enough vegetation and humidity to keep the air relatively dust-free, and if the air composition was similar to that of Earth, would that planet necessarily have a blue sky, like ours?  Under what conditions could such a planet have a different color sky (yellow, for example) and still support terrestrial-type life?

Not necessarily blue, although pure nitrogen, ozone, methane and hydrogen- cyanide  all appear as various shades of blue (assuming the incoming starlight is white). Nitrogen dioxide would be a deep brownish red. Some organics and chlorine dioxide are yellow, whilst chlorine is yellowish green. There are many other coloured gasses, but they are not likely to form planetary atmospheres. However, the question implies vegetation, and hence Earth-like photosynthesis, in which case it also implies an Earth-like atmosphere, in which case a normally pale-blue sky is inevitable.


What was there before the Paleolithic and where really did life start for us humans, not like homo? I am asking myself something very interesting for me (I am not expert in this topic).
I read many websites about human history and every website says that there are some periods in the human history.
For example: periods

Paleolithic

Mesolithic

Neolithic

Bronze Age

Iron Age
etc.

Generally it is estimated that humans evolved on Earth between 200,000 and 300,000 years ago.
I know we are homo sapiens, what I am trying to understand is what is the first step for humans. I read about Australopithecus and the "Australopithecus" evolution of the homo on the Earth. I know they are not considered human like us, but, where and when is the first step around the Earth in history where life for humans starts?
humans in the earth
homo
homo
info
Hominidae
I am not expert in this topic I hope somebody could give me more information about this question.

The partition in stone - bronze - iron is an early 19th century thing invented to explain to pupils the findings in north-western Europe. It has little modern day relevance.

Paleolithic (European ! Africa has a different chronology and the below is not generally applicable !) is a hand wavy container expression for anything from the first signs of human presence in Europe ~1.7my up to and including the last glacial maximum (lgm), and mostly refers to the life and strife of humans in the cold steppe. It encompasses different human species and subspecies, and in itself is divided into lower-, middel- (starting ~250,000) and upper paleolithic (~45,000) following stratigraphic expressions that what is above is regarded as being younger than what is below.

On a high level, lower paleolithic (again: Europe !) is generally connected with the presence of Homo habilis and erectus and ante-neandertals (i am being a "lumper" here), the middle paleolithic is the time of the Neandertals (Homo sapiens neandertalensis), and the upper paleolithic deals with the likes of us, Homo sapiens sapiens. All these units have further subdivisions based on tool technology and -industry. As an example, the upper paleolithic is further subdivided in (old to young) Aurignacian, Gravettian, Solutrean (around the lgm) and Magdalenian with their respective clearly distintctive stone and bone tool industries, and these have further local or regional expressions that deserve more detailed description.

With the onset of re-forestation human subsistance changes funtamentally. This is the time of the hunters and gatherers of the forests, the Mesolithic period in Europe, a relatively short period broadly taking place between ~9000 and 5500 BC. It lasts until and partly side by side with the incoming Neolitihic, the revolutionary (but not abrupt) change to farming, animal husbandry and settledness. The latter started over a few thousand years in Anatolia/Fertile Crescent/Levant between 11000 and 8000 BC (simplifying here for the sake of brevity).

Then it gets complicated. Metal (copper) has already been in use since in the Neolithic (see Chalcolithic), use of bronze starts in the middle east ~3200BC, and spreads north, arriving in central Europe around 2200BC. But it is not used everywhere (example: Egypt). Use of iron starts ~1200BC, arriving in central Europe ~800BC.

Please keep in mind that there are huge regional or local differences with their respective own chronologies, because human subsistance and so the use of tools has never been uniform.

Regarding humans: We can speak of "humans" and the genus Homo with the onset of tool making ~2.6My ago, Oldovan stone tool technology in East Africa, though in these times there is not always and generally a link between a human species and a certain stone tool industry. The case of human speciation is complicated, and got even more so with the use (ancient) genetics as a tool for analysis. Though the latter is still a developin science, it looks like there have always been multiple species or subspecies around until recently. Today there is only one human species remaining on earth, and it is unclear how long they'll make it.

Pls. let me suggest to search the Biology departement here (or elsewhere) for an up-to-data primer on human species and subspecies.

Ok, questions ? :-)

Edit: since the original question has been clarified, i'd like to add something. First, there is no such thing as a single step that defines what an anatomically modern human (amh) really is. Evolution does not jump ! It is a gradual process of speciation, of adaptation to a niche. It may take place in a limited geographical region if individuals don't move far or over a large aerea, even over continetal limits if individuals or groups move in an out. As that's what humans did, things aren't exactly simple and in constant flow.

Find places of early amhs show that they developed, just like other humans before, in Afrcia, but rather distributed than in a single spot. Jebel Irhoud is up to now the oldest specimen with traits of amh, for the splitters among us in the field between h. heidelbergensis, rhodesiensis, neandertalensis, sapiens ...

But there is maybe another thing to your question, which is called "behavioural modernity". People using personal adornement, creating art objects like figurines, paintings of scenes from real life or more abstract symbols on cave walls, carving and painting mussels, etc. For a long time this was attributed to amh in Europe alone, starting with the arrival of the first groups of modern humans ~45,000 years ago. But recently, this was questioned, with the findings of traces from Neandertal find sites hinting to abstract thinking much earlier, 65,000BC in Gibraltar (Gorham Cave) and 115,000 in southern Spain. So, from a cultural point of view, there is no real "unique feature" in favour of amh any more, the differences are floating, not "carved in stone" (haha).

This is all in flow and debated. There'll surely be more news on all this soon (tm).


How much energy is required to hold the earth’s atmosphere up against the forces of gravity? My understanding is that the earth’s atmosphere was originally formed by the molten earth itself, and the sun was 70% weaker that present. Then as the earth cooled the energy required to keep the atmosphere up against the force of gravity was provided by incoming solar radiation.

Presumably this energy is in excess of that incoming from the sun and outgoing to space. Can anyone tell me how much solar energy is required to kind of keep the sky up.

So say we started with the present sun and current atmospheric mass with the earth at its blackbody temperature with the current solar energy hitting its surface - 255K. How much additional solar energy is required to lift those gasses not already off the ground at this temperature up into the sky?

I realise that the current energy reaching the earth’s surface is due to the atmosphere absorbing some of the energy but I’m interested in the excess energy in the earth/atmosphere system required to keep the atmosphere up.

I can not comment on whether your understanding about the formation of the atmosphere (your first paragraph) is correct. However, given that the complete atmosphere lied at the surface at some point, it is possible to calculate the amount of energy to lift this towards its current state.

The calculations will be done for a small surface area of one meter squared and choosing values corresponding roughly to the global averages.

First we calculate the gravitational potential energy of the atmospheric column as follows

$$ E_{atmos} = m_{atmos} \cdot g \cdot h_{atmos}$$

with $h_{atmos}$ the height of the center of mass of the atmospheric column. Surface atmospheric pressure ($P_{surface}$) is the force resulting from the mass of the atmospheric column under gravitational acceleration. Therefore, the above can be rewritten as

$$ E_{atmos} = P_{surface} \cdot h_{atmos}$$

The centre of mass at the atmosphere is approximately at $h_{atmos}$ = 5 km (one can look at maps of the 500 hPa geopotential heights for example). For surface pressure we can take the standard atmosphere (101325 Pa). Plugging in the numbers:

$$ E_{atmos} = 101325\,Pa \cdot 5000\,m = 5.1e8\,N m^{-1} = 5.1e8\,J\,m{-2}$$

To get to the total energy, this number needs to be multiplied by the surface area of the earth. However, it is more insightful to get a certain timescale out of this value. We can calculate how long it would take for the sun to deliver this energy to the earth's surface.

$$ \tau_{sun} = \frac{E_{atmos}}{SI_{avg}}$$

With $SI_{avg}$ the global average incoming solar radiation (not taking into account atmospheric absorption). Plugging in the values

$$ \tau_{sun} = \frac{5.1e8\,J\,m{-2}}{340\,W\,m{-2}} = 1.5e6 s = 17.5 days$$

So it would take about 17-18 days for the sun in order to 'lift up' the atmosphere from the surface to its current height.


What kind of temperature unit is "cK"? An article about ocean temperature history/modeling has this plot:

Where the units are given as cK.
cK does not seem to be a standard temperature unit and Google searches return millions of results that don't seem relevant.
While it might be "fun" if the temperatures shown were Kelvin, centi-Kelvins, or even Celsius, that seems unlikely as we are all still alive.
So, what is cK as shown on the graphic, and how do you convert the temperatures shown to degrees Celsius?
If the axis is really some delta temperature, what is the zero point?

It actually is the common centi- prefix, but applied to temperature changes in units of Kelvins rather than absolute temperatures. The research as quoted by the Daily Mail obscures this by simplifying the results for a general audience.

The analyses and results are much more clearly described in the original refereed journal article, which clearly refers to changes in temperature - see in particular the discussion of their Fig. 1 (reproduced below), which explicitly refers to rates of temperature change of 0.1 and 0.02 °C at depths below 2000 m over the previous century, corresponding to cK contours of ~10 and near zero in the corresponding section of that figure.

So, the contours in the figure quoted by the Daily Mail represent changes in temperature between measurements made in the 1870s and the early 2000s, in units of 0.01 K (or equivalently, 0.01 °C).

Figure 1 from Gebbie and Huybers (2019), https://doi.org/10.1126/science.aar8413 - reproduced here for non-commercial use.


Giant Cavern the Size of Manhattan Under Thwaites Glacier in West Antarctica Avery Thompson writes in Popular Mechanics

  In Thwaites’ case, that radar uncovered a gigantic cavern between the glacier itself and the bedrock below it. That cavern is likely filled with air much warmer than the surrounding ice, triggering faster melting of the glacier than would happen otherwise.

From the cited NASA release I would have assumed that the cavity is full of water (if only because ice is not strong enough to support such a large air-filled cavern).

Is it air or water?

The cave is created by warm water, so the ice has been replaced by the water.

It is common to find areas under the front of glaciers that have been melted away by warm water.

Warm water here means just warmer than the ice.

The thing that's making this news is the scale of the area that has melted away.

This is the situation scientists have been predicting to happen as our planet warms, water advancing inland below the ice and possibly making the glacier move faster.

Ice that is connected to bedrock will create friction making the ice move at a slower pace. When ice is no longer connected to the bedrock under it will loose this friction and possibly speed up the flow.

This might be one of the tipping points making the ice melt at a faster pace.

https://en.wikipedia.org/wiki/Thwaites_Glacier

As fred say in his comment ice moving over bedrock will create friction this friction will create a layer of liquid water lubricating the ice and make the ice flow faster,And when ice melts at the surface of the ice the melt water will run down cracks in the ice and reach the bedrock lowering the friction even more.


Is there a freely usable map of Earth cave systems and/or density? I am considering creating some sort of model of the Earth using real world collected data. Things like worldwide height maps certainly exist, as do many maps of minerals, rainfall, etc.

Are there worldwide data available on underground cave systems?

By density, I mean the likelihood of there being a cave in a certain place, given coordinates. Hence it does not have to be exact (although this would be better), but a mathematical model or approximation would be useful if the former does not exist.

You might like to check World Cave List which has a pretty extensive list of caves, their depths, and lengths.
For example:

This list has been automatically produced from our World Caves Database.
Total depth and length of all caves currently collected in the database:

Number of caves = 2424
Caves deeper than 300m = 1075
Caves longer than 3kms = 1628
Cumulated depth = 648 932 m
Cumulated length = 17 930 192 m


Why is the troposphere 8km higher at the equator than the poles? Fundamentals of atmospheric modeling show that the height of the tropopause depends on the location, notably the latitude, and that the troposphere is roughly 8km higher at the equator than the poles depending on the time of year.

First guess was that the temperature of the would impact its density (for example, here's the data for -50° and 50° c) - though this does not appear to show the cause of why is the troposphere 8km higher at the equator than the poles.

Why is the troposphere 8km higher at the equator than the poles?

To start with a definition, the tropopause is the boundary between troposphere and stratosphere; within the troposphere temperature decreases with increase in altitude (the temperature profile is dominated by radiative heat from the Earth's surface), whereas in the stratosphere temperature increases with altitude (the temperature profile is dominated by solar radiation).

Increases in troposphere temperature are associated with increase in tropopause height. Factors in play include the amount of water vapour being evaporated from equatorial seas; interannual variations in tropopause height can result from both local and large-scale driving forces. The tropopause responds to the average temperature of the entire underlying layer: this is higher at the equator and lower at the poles.


Does the geothermal activity influence the climate in Iceland? Iceland has a much more temperate climate than its latitude (~65 degrees) would suggest, thanks to the North Atlantic Current bringing relatively warm water/air so far up north.

There is a lot of geothermal activity (geysers, volcanoes) in Iceland, because it lies on the boundary between the Eurasian plate and the North American one. They generate a lot of heat, at least locally. I'm curious and haven't made any calculations (I wouldn't even know where to begin) but does this activity influence the climate in Iceland in a meaningful way? E.g. without the geothermal activity, would the mean annual temperature drop, and if so, by how many degrees?

Since micro climates of a location are mostly based on temperature and precipitation patterns, I would suggest that yes, if focusing on precipitation alone, one would expect rising heat from geothermal activity off the surface as a variable to affect the formation and release of precipitation. But one would have to check what part of the island geothermal activity was highest as the jetstream would drag weather west to east if nearby, and given that it is a small island, may not record such an occurrence as a significant effect from the rising heat available around Iceland. I did find this article which is somewhat supportable of a heat increase around Iceland due to geothermal activity > Douglass/Patel and Knox, 2005. GEOPHYSICAL RESEARCH LETTERS, VOL. 32 https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004GL021816


Locating shearlines or deformation zones in a map I am looking for a way to detect shearlines or confluence regions such as that shown in the figure below (red dashed line).

I was able to derive the stretching, shear, and total deformations.

The  shearline coincides with the region of maximum stretching deformation.

Currently, I am drawing the shearline manually. But I am wondering if there is a way to automatically detect this line or the deformation zone.

I am thinking of two ways

Looking at the dynamic variables (i.e., stretching deformation)
Using Python's pattern recognition function to detect this kind of pattern.

Problems

Any suggestions on how I can detect this automatically. I don't know how to implement the above methods. I am not sure what criteria to set in order to draw the shearline.

I am also new to Python so I don't know how to implement the second one.

I have wind data and calculated deformations in netcdf format. I uploaded the files to the following links:

UWND

VWND

Link to calculated deformation parameters

The deformation parameters are calculated this way:

where A, B, and V are the shearing, stretching, and total deformation parameters.

In the deform.nc file, shear_sph(shearing deformation; A in the equation above); stretch_sph(stretching deformation; B in the equation B); deform_sph(total deformation; V in the equation above).

I'll appreciate any help about this matter.

SUMMARY

I am looking for the NCL Shear, stretch, deform functionality in Python. Are there any existing software packages or do I have to write my own ?

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

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

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

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

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

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

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

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

UPDATE

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

Sample code

 from spharm import Spharmt

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


How do we measure the total heat content contained in ocean and atmosphere? This is similar to How is ocean heat content measured?, but here I'm thinking about the total content rather than fluxes in the content between land and ocean.

The heat content of a substance is known as enthalpy.  The international convention used for the Thermodynamic Equation of Sea Water is maintained by the Joint Committee on the Properties of Seawater.

Equations for calculating the thermodynamic variables of sea water, humid air and other substances are set forth in the Numerical implementation and oceanographic application of the thermodynamic potentials of liquid water, water vapour, ice, seawater and humid air – Part 1: Background and equations by R. Feistel, D. G. Wright,†, D. R. Jackett, K. Miyagawa, J. H. Reissmann, W. Wagner, U. Overhoff, C. Guder
,A. Feistel, and G. M. Marion. Enthalpy of sea water is discussed in section 4.3.

One would need temperature, pressure and salinity information for each point in the ocean, and integrate specific enthalpy over the entire volume of the ocean.


How can I calculate the average latitudinal temperature anomaly between a city and all other locations along the city's latitude? E.g. How could I calculate the average temperature anomaly between, say, Boulder, CO and all other locations along the 40N parallel?

What about Boulder, CO, and all other locations along 40N at Boulder's altitude?

What datasets would I need, and what resolution would they have? What would be the advantages/disadvantages of these datasets?

I think you'd be looking for a gridded dataset, where data has been interpolated to a grid of the Earth, rather than one that has data at particular points (eg. stations). I'm probably not the best person to recommend one particular gridded set over another, but a good start might be the Met Office's HadCRUT4 set, which combines gridded surface temperature data over both land and sea.

I haven't looked at HadCRUT4, but I daresay it'll come (either in plain text or NetCDF format) as a lat/lon/time array (grid), and if you're interested in a particular latitude, you should just be able to read off a latitudinal row. Then you can look at it however you like: subtract the temperature of the point closest to your city from the rest to get an anomaly right across the latitude, or average the whole thing (a zonal average) and subtract the city baseline from that. Hope that helps :)

Edit: HadCRUT4 is a monthly dataset. If you're looking for more temporal resolution (the weather tag suggests to me that you are), I'm not sure what availability among gridded sets is like, but you could try taking a station set (eg. HadISD, which is hourly) and gridding it yourself.


Relation between direction of shear and tropical cyclone formation In an earlier question/answer Why are there no cyclones in the southern Atlantic basin there was a point raised about direction of              shear(abetting/retarding) cyclone formation. It appears westerly shear hinders the formation of tropical cyclones and easterly shear enhances it. This should mean that cyclone formation is only allowed over monsoonal regions. Is that accurate ? From this article - Tropical wave dynamics and Heatinduced
steady response.

it appears that in the presence of westerly shear Rossby waves have large amplitude at the upper troposphere whereas in the presence of easterly shear Rossby waves have large amplitude in the lower troposphere. Given that the precursors of tropical cyclones are equatorial waves can a clarification be provided in this regard ?

The direction of the vertical shear plays a large role in cyclogenesis, where easterly shear tends to enhance the formation of cylones and westerly shear tends to suppress it (Tuleya and Kurihara, 1981).

Internal waves are responsible for large transfers of energy across vast distances. Therefore, they play important roles in a wide array of atmospherical (and oceanic) processes, such as tropical cyclogenesis (TC). These waves are capable of depositing energy that was created far away in an already active region. This energy may manifest itself in the form of an easterly shear. Depending on the type of wave, there is evidence that some waves play an important role in triggering cyclogenesis.

  It has been known for decades that about half of the hurricanes in the
  North Atlantic form from African waves (e.g., Frank and Clark 1980),
  and there have been many studies of this type of genesis (e.g.,
  Carlson, 1969; Landsea, 1993; Thorncroft and Hodges, 2001; and
  others). Molinari and Vollaro (2000), Mal- oney and Hartmann (2001),
  Liebmann et al. (1994).

There are three main classes of equatorial internal waves to consider when it comes to TC. Namely, the MJO, equatorial Rossby (ER) waves and other large wavenumber, high frequency waves that propagate towards the west (mixed Rossby gravity waves). Note that equatorial Kelvin waves are thought to have little effect on cyclogenesis. This animation from NOAA shows the internal waves (as OLR anomalies) over a period of 40 days:

Specifically:

the MJO and ER waves produce easterly shear anomaly at the generation location, while the MJO tends to reduce westerly shear over the atlantic.

  the MJO enhances both the convergence and the low-level rotation at the genesis location. [...] shows a tendency for the MJO to cause easterly vertical shear anomalies at the genesis location in most basins, and this would tend to be favorable for storm formation.  [...] The ER waves also appear to enhance genesis by increasing both the
   large-scale rotation and convection, as in this band the storms form
   within the low-level cyclonic gyre and about two days after the wave’s
   convective anomaly first reaches the genesis point. The ER waves also
   cause significant easterly shear anomalies at the genesis locations in
   about half of the basins.

Basically, internal waves enhance the formation of cyclones by increasing the vorticity of the local flow.

References:

Anantha R. Aiyyer and Chris Thorncroft, 2006: Climatology of Vertical Wind Shear over the Tropical Atlantic. J. Climate, 19, 2969–2983.

Tuleya, Robert E., and Yoshio Kurihara. "A numerical study on the effects of environmental flow on tropical storm genesis." Monthly Weather Review 109.12 (1981): 2487-2506.

William M. Frank and Paul E. Roundy, 2006: The Role of Tropical Waves in Tropical Cyclogenesis. Mon. Wea. Rev., 134, 2397–2417.


Do we know how large deposits of methane clathrates were formed in permafrost regions? We can see that there are large buildups of methane clathrates in permafrost regions. This seems different to the buildups of natural gas which fracking releases, which appear to have just come from escaped gases from oil/coal deposits.

My question is: Do we know how large deposits of methane clathrates were formed in permafrost regions?

Rotting vegetation generates methane, or marsh gas as it is sometimes called. The most obvious method by which clathrates were formed in Arctic regions is that many years ago during summer and autumn, rotting vegetation produced methane, which combined with water at cold temperatures to produce methane ice, otherwise known as clathrates. Extremely cold temperatures are not necessary to form clathrates. They form at the bottom of deep seas, where the temperature even in the tropics is a constant 4 or 5 C. There are huge deposits of clathrates on the deep ocean floor, and there has been talk of exploiting them commercially.

A theory to explain the Permo-Triassic extinction event of 250 million years ago hypothesises that super-eruptions in the Siberian Traps super-volcano raised temperatures enough to release vast quantities of methane from tundras and ocean beds, so that with the additional greenhouse gas the average temperature rose by about 10 C, causing the extinction of nearly 90 percent of all large animals, a greater mass extinction than the better known event at the end of the Cretaceous.


Quartz mineral category Different learning resources categorise quartz differently. Strictly speaking, is quartz a silicate mineral or an oxide mineral?

Quartz is a silicate at the most used Dana and Strunz classifications of minerals.
Several websites and mineralogists classify it as silicate.
The structure of quartz is similar to other silicates with Si-O tetrahedres.

Quartz structure. quartzpage.de


How much do anthropogenic carbon aerosols (soot) affect the atmospheric radiation budget? Anthropogenic carbon or black carbon (BC), particularly soot, is a product of many human activities, a major one being incomplete combustion of hydrocarbons. The occurrences of soot are mainly in cities, although there have been observations of these aerosols occurring in sparsely inhabited, yet environmentally sensitive areas, such as polar ice caps.

As such, it is a critical component of atmospheric aerosols affecting the Earth's atmospheric radiation budget.

How much do anthropogenic carbon aerosols affect the atmospheric radiation budget?

For the purposes of the answer, anthropogenic carbon sources, will be referred to as BC (black carbon).

The effects of BC on atmospheric radiative forcing has come under considerable scrutiny in the past few years, several studies have sought to determine exactly the effect of this aerosol has on the Earth's radiation budget.  A major study was performed by Bond et al. (2013), in the article Bounding the role of black carbon in the climate system: A scientific assessment.

A summary of the emissions and effects are shown in the image below:

Caption: Schematic overview of the primary black-carbon emission sources and the processes that control the distribution of black carbon in the atmosphere and determine its role in the climate system. (Bond et al. 2013)

Their findings was that the total direct forcing of BC, independent of co-emitted aerosol species and including all sources (direct emissions, pre-industrial background, cryosphere and clouds) is +1.1 Wm-2 with an uncertainty range of +0.08 Wm-2 to +2.1 Wm-2.

This places BC itself as the 2nd most important anthropogenic emission, behind $\ce{CO2}$.

However, when the total radiative effects of BC with co-emitted aerosols, including organic carbon sources are taken into consideration, there is a slight cooling effect, with the total direct radiative forcing of -0.06 Wm-2, with a greater uncertainty range of -1.45 Wm-2 to +1.29 Wm-2.

Bond et al. 2013.

A major source for the uncertainties is described by Bond et al. (2013) as being due to a lack of knowledge of how black carbon and co-emitted species interact with clouds.

In summary, from the research that Bond et al. (2013) performed, the presence of black carbon itself causes a significant positive radiative forcing, but when considered with its co-emitted aerosols, there is a slight negative radiative forcing.  However, the uncertainties calculated demonstrate that there are still mechanisms that are not completely understood.

Image permissions (from the linked article): This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.


What time and date is the sun directly overhead a given place on Earth? In different regions, the sun is directly overhead at different times of the year. When do these event happen?

Having the sun directly overhead can happen only between the Cancer and Capricorn tropics. That is, only the places between 23.5° of latitude north and 23.5° of latitude south.

On the Cancer tropic (23.5° latitude north) it will happen once every year, on the day of the northern hemisphere solstice (about June 21st).
On the Capricorn tropic (23.5° latitude south) it will happen once every year, on the day of the southern hemisphere solstice (about December 21st).
On the equator it happen twice every year. One on each equinox (about March 21st and September 21st).
For any other given place between the tropics, it will also happen twice every year. On the days when the Declination of the sun (a coordinate in the sky analogous to latitude on the Earth), matches the latitude of the place.  Various formulas to calculate the declination to various precision can be found at Wikipedia.

This figure would help to visualize the situation:

The configuration depicted, will happen on the southern hemisphere summer solstice (about December 21st): The only day of the year when the sun passes exactly overhead on the Tropic of Capricorn.

When it comes to what time of the day it will happen, it won't happen at local noon (see this question), the exact time requires a lengthy calculation (see this), but a good estimate would be:

GMT - TimeZone + Longitude/15

Where GMT is Greenwich Meridian Time (the same than Universal Time for this effects), TimeZone is the time zone you are at. For instance -7 for the Pacific Time Zone in North America. And Longitude, is the longitude of the observer (expressed as a positive value for East longitudes and a negative value for West ones). The error of this estimate can be in the order of 15-20 minutes.


Is Atacama Desert cloudy or cloudless most of the year? I saw many contradictions everywhere.

For example, my geography textbook says that Atacama Desert is a desert where foggy weather is common, and it persists almost the whole year. Whereas every astronomer will say that it is the place with least clouds and fog, which is why it is one of the best places for astronomical observations beyond the dark sky.

So, which is true? And if one of them is not true, then how did it gain popularity at all?

The Atatacma desert comprise a very large area of more than 100,000 square kilometers, that host very different climates. The main factors driving the climatic variability are the distance to the Pacific ocean and elevation. This latter factor is very strong, as the Atacama desert covers a formidable elevation range, from sea level to almost 7,000 m, at the summit of Ojos del Salado, the highest volcano in the world.

The highest areas, that also happen to be further of the sea, are the driest and less cloudy. The weather pattern you mention of common foggy conditions only happens very close to the coast, extending inland only about 10 km or a few tens of kilometers trough low lying valleys. And is produced by fog that forms over the sea penetrate inland. It is called camanchaca, but it doesn't cross the coastal mountain range.

The pattern of cloudiness throughout the desert can be visualized using a dataset produced by NASA's Surface meteorology and Solar Energy, in particular the satellite derived Monthly Averaged Clear Sky Days (i.e. cloud cover less than 10%) over a 22-year period (July 1983 - June 2005). It has a resolution of 1°, so about 110 km here. That means that the central area of the desert is covered by just 12 cells, nevertheless it will be enough to see the pattern I mention.

The following figure shows the central section of the Atacama dessert using official IGM 1:500.000 maps in the left. And the mean clear skies dataset on the right for the very same area. The numbers refer to the 22-year mean clear sky days per month.

Now the interpretation will be easier if we overlay those two figures as in one as shown below:

From there you can see how there is a fairly large variability in the amount of clear sky days. From a few at the coast, to about a week over the coastal mountains to about two weeks east of the coastal mountains. Peaking at 17 days per month over the Salar de Atacama (a value surpassed only by the Sahara desert and the interior of Antarctica). Then, it is no coincidence that ALMA observatory, the largest radio telescope in the world, is located there, just east of the salar de Aatacama.

As mentioned before, topography is the main factor controlling this pattern. Even close to the coast, some mountain tops can have remarkable clear sky records, as is the case of the 2,635 m high Mount Paranal were the VLT observatory is located at just 15 km from the coast (falling on the lower left cell with 9 clear sky days in the figure above).

Finally, going back to your question: Both the astronomers and your geography text book are right, but each one is referring to a different area of the Atacama desert. And looking at the above data, it is right to say that the coastal part of the Atacama desert is cloudy most of the year. However, a fairly large area in the interior is cloudless most of the year.


Why aren't there ultra acid igneous rocks? In my study, I found that in volcanoes when the magma is going up it formed different types of rocks. There are basic, acidic and ultrabasic.
My question is why isn't there ultra acid igneous rocks formed?

Warning: not an igneous petrologist here, I'm more interested at what happens to magmas once they reach the surface than all the processes they might have encountered along the way...
That being said, I think it has to do with the source of magmas, that is the mantle, which is itself mafic. Hence partial melting of the mantle generates mafic magmas. These magmas can then differentiate into more "evolved" (i.e. intermediate or felsic) ones. But there are limitations to these differentiation processes. With fractional crystallization you make mafic minerals, pumping Mg and Fe from the melt and leaving it silica-enriched, but you will never reach a 100 % silica melt (after all mafic minerals are still silicates, they need some Si in their structure). Same with crustal assimilation: the continental crust is only andesitic (intermediate) on average (Rudnick 1995), and even with assimilation of more felsic (i.e. granitic) portions you would never reach a 100 % silica melt. The starting material is too mafic.

On a more "philosophical" note, I would add that despite the absence of the term "ultrafelsic" in the literature, one could still consider some rocks as being ultrafelsic. If you look at the chemical classification of volcanic rocks (Le Maître et al. 2002), the four fields are separated like this (silica content in wt%):

Ultramafic: SiO$_2$ < 45
Mafic: 45 < SiO$_2$ < 52
Intermediate: 52 < SiO$_2$ < 63
Felsic: SiO$_2$ > 63

So everything above 63 wt% SiO$_2$ is just "felsic". But there are some rhyolites with more than 76 wt% SiO$_2$ (some of Mono Domes for instance, see Table 2 in Bray et al. 2017)! So there is no distinction between a low-silica dacite and a high-silica rhyolite: they all are just "felsic". That doesn't mean they are not different, just that we never bothered making a fifth filed. But those fields are just arbitrary, we could have split the silica spectrum into twelve fields, or just two.


Why doesn't the 71% water of the earth dry or evaporate? Perhaps a simple question, we know 71% of the earth's surface contains water as oceans. If Earth's age is 4.543 billion years, then I guess it should be decreased with drying or should have been dried so far. Why doesn't it dry or decrease?

If we put some water in sunlight, it evaporates. The oceans are the chief source of rain, but lakes and rivers also contribute to it. The sun's heat evaporates the water.

So I wonder why doesn't the 71% water coverage not evaporate, decreasing until gone? Why is it still 71% after billions of years? Does water keep coming from somewhere? Or, does moving water not evaporate? Why is it still here?

There are two ways this problem needs to be looked at.   The first is more astronomy than Earth science.  The Earth as an entire system is largely contained.   Its gravity and magnetic field retains nearly all of its elements.   Earth does lose hydrogen and helium and cosmic rays will split water molecules leading to a loss of an impressive amount of hydrogen and as an indirect result a loss of water, but this is loss irrelevant compared to the size of the oceans.    More detail here.  Space dust, comets and asteroids contain water so some water is returned from space too.

By the upper estimate in one article, 50,000 tons of hydrogen per year works out to about 450,000 tons of water lost every year. (and 400,000 tons of oxygen added as a result).    Compared to the mass of Earth's oceans those numbers are small.  450,000 tons per year, or 450 trillion tons over a billion years is nothing compared to the 1.3 million trillion tons of water Earth has in its oceans.   By the highest estimate, it will take 30 billion years at the current rate and at the Sun's current luminosity for Earth to lose just 1% of its oceans.  (Will look to update with other estimates).

As for the rest of the question, once we recognize that loss into space is insignificant, then virtually all water is continuously cycled though the water cycle or hydrological cycle.   Very little water gets destroyed or chemically transformed.   Nearly all of it, even of millions or billions of years, evaporates, or, turns into ice, or gets absorbed by plants, or seeps underground, but it always returns.   Evaporated water returns to Earth as rain.   Water that gets frozen on the ice caps eventually melts back into the oceans.   Water absorbed by plants or that seeps underground does eventually get returned to the surface by plate tectonics or volcanism.   Plants that store water return it when the plant is eaten.  Water is very hard to destroy, so it stays remarkably constant on Earth over time.


What atmospheric radiative effects, if any, do contrails cause? First of all, this question is not about the conspiracy theory based chemtrails as in the linked question, from the answers there and from the question Equation for predicting contrail formation I understand how contrails form and how they could be predicted.  Also, the evidence (as in the links and discussions within the answers to the linked questions, reveal that much of the chemistry of contrails is water and some soot.

Observations of contrails (from the ground and from satellite imagery) in the webpage Contrails by Professor Steve Ackerman of the University of Wisconsin, he notes that contrails

  rapidly dissipate or spread horizontally into an extensive thin cirrus layer.

and also states that

  NASA and the DOE are sponsoring a research program to study the impact contrails have on atmospheric chemistry, weather and climate.

Given it is long known that clouds affect the atmospheric radiation budget, is there evidence of atmospheric radiative effects of contrails?

We had a surprising opportunity to study this very question during the period of September 11-14, 2001, when all air traffic was grounded across the United States.

The research was inconclusive, but they found that there was a 1.8 degree celsius increase across the US during this time frame compared to the three days before and after that time frame.  However, this shift may have been coincidental.  It was found by others that this temperature shift was within the normal range for the time of year.

Ultimately, the question of whether these high-level clouds impact the weather and temperatures is still an open debate.

The source for this answer is from a Nature.com article.  However, they take their sources from the original research:

Travis, Carleton, & Lauritsen showing contrails have an effect
Hong, et al showing contrails do not have an effect


Is there a website where I can freely download monthly average temperatures per city? I am building a garden calendar app, that uses the average temperature for each month to create a calendar. Are there websites that allow me to easily download this data in json format?

Out here in the Western U.S. the Western Regional Climate Center offers such historical data for download.  If I recall, their downloads are in .csv format, not .json. However, converting data formats is relatively straightforward for GIS software, such as QGIS.


Do higher global temperatures increase volcanic activity and earthquakes? I've read that when global temperature increases significantly, liquid magma can make it's way closer to the surface of earth, causing increased volcanic activity and earthquakes.  Is there scientific evidence of this?

Define 'significant' effect. Yes, in principle, both volcanoes and major fault lines can, in some cases, be affected by higher global temperatures. It works like this: higher temperatures cause glacial melt and thermal expansion of seawater, which causes sea level to rise. In ocean sediments and coastal aquifers this raises the hydrostatic pressure, increasing groundwater circulation, and hence providing more 'lubrication' to fault planes, and increasing the seepage above magma chambers. However, the effect is small.


Incised valleys and hydrocarbon potential The Wikipedia page on sequence stratigraphy states:

  Sandstone bodies associated with incised valleys are good hydrocarbon
  reservoirs.

Can anyone elaborate on this statement and the reason behind it?

Sandstone bodies in incised valleys can be good hydrocarbon reservoirs.

Incised valleys form on the coastal plain and/or continental shelf during a fall in relative sea-level by a combination of fluvial and marine processes (e.g. fluvial erosion, headward erosion). Here's some coastal plain incision by the Orari River in New Zealand — look at those sand bars!

With the caveat that incised valleys can be filled with mud (non-reservoir) too, there are a few reasons why lots of hydrocarbon fields have been discovered in incised valleys:

Incision spatially focuses sediment delivery to the shelf-edge and slope. So if coarser sediment (needed for porosity preservation and therefore a reservoir) is stranded on the shelf, there'll be more of it the incision.
Sediment only stays where there is 'accommodation'. A big hole that wasn't there before (like a new valley) is accommodation.
The valley acts as a container, so channels tend to have a high ratio of thickness to width.
For these reasons, valleys sometimes fill with multi-storey sand/mud successions. Lots of reservoirs!
After the sea-level fall comes sea-level rise, or 'transgression'. Everything is flooded, and you're back to a marine environment. There's mud everywhere and so now you have a top seal.
It's relatively easy to connect a valley to a marine source rock, either directly updip via porosity, or via faults or other conductors.
Unlike basin-floor fans, which can also be good reservoirs, incised valleys are on the shelf and might still be beneath the modern shelf. This means there are many more options for drilling wells targeting them, and it's much cheaper and easier.
Compared to unincised systems, incised valleys are relatively easy to spot in well logs and even on seismic. So they are conspicuous, and easily diagnosed.
Since the start of the incised valley craze in the 1990s, incised valleys have been deliberately targeted, so there's probably some sample bias: lots of wells means lots of discoveries!

The last little gem is that, even if there's no luck exploring on the shelf, incised valleys might mean that coarse sediment has been delivered to the basin floor, opening up a promising play there too.


Why is ozone a greenhouse gas if it absorb UV radiation? Ozone is considered a greenhouse gas, even though it absorbs shortwave (UV) radiation from the Sun, which does not fit with the definition for greenhouse gases, where they are supposed to:

  "absorbs and emits radiant energy within the thermal infrared range"

(Quote from Wikipedia)

PS: This is an adaptation of a question asked in the private discussion board of a first year university course on Earth Sciences, that I'm copying here so a wider community can benefit from it and the corresponding answer. As suggested by the moderators, I'll donate any reputation received from this question in the form of a bounty awarded to exceptional answers (in particular by new or low-rep users).

Ozone is indeed a greenhouse gas. But not due to its capacity to absorb/scatter UV radiation, but instead due to its capacity to absorb infrared radiation. In contrast with other greenhouse gases (Like $CO_2$ or Methane), the spectral absorption bands of Ozone are not confined to the infrared part of the spectrum, there are ozone absorption bands also the UV too.
However, if ozone were capable to absorb only UV radiation, it would NOT be a greenhouse gas. Because the Earth's surface do not emit UV radiation.

Therefore, the greenhouse gas properties of Ozone do not come from its ultraviolet absorption bands, but instead from its infrared absorption bands, that you can see in the following infrared spectrum obtained from NIST:


Is there a risk with putting out smouldering underground coal reserves? According to this Discovery News video about Centralia Pennsylvania and other underground coal fires, there are many smouldering underground coal reserves making the land above it uninhabitable to humans.

One possible solution I thought of for extinguishing an underground coal fire might be to invent the technology to affect weather patterns and then create a continuous rainfall overhead so the water will drain down to the coal putting it out.

However, it's a very risky for people to make any big change to the Earth without first getting a research group to very carefully research whether it might cause a nasty problem later that they didn't think of. For example, maybe if every time a coal reserve catches on fire, people put in out, then the earth will start producing coal faster than it's getting depleted by natural process and eventually it will have such a huge buildup of coal that if a it catches on fire and doesn't get put out right away, it will grow too big to be able to put out and will then spread all over the entire continent.

There isn't a risk to putting it out other than the attempt failing, it is just impractical to do so once a large coal vein catches fire.  You can read more about the Centralia, PA coal fire on wikipedia which sits on top of anthracite coal veins that have been burning for a little over 50 years and are likely to continue to do so for a few hundred years.

Your proposed idea of "inventing weather modification" is not practical. While we have the technology to seed clouds and produce localized rainfall this still depends on the amount of water vapor in the air.  You can bypass evaporating water into the atmosphere, seeding clouds and raining and instead just pump water into the mines and flood them.  That will be much more efficient at getting water into the mines.

So why don't they flood the mines?  That too is not practical, and if memory serves me right, they did try that in Centralia.  The problems are

Mines open into cave systems
Water can flow through very small openings
Coal needs only be hot and in the presence of oxygen to burn.

This means you'll need to keep the coal underwater, isolated from oxygen until you can dissipate all of the geothermal heat mass that has built up in the surrounding rocks from the burning coal.  You'll also have to make the mine water tight in order to flood it.  You'll also need a way to deal with your flood water evaporating and steam build up.

Faced with the realization that extinguishing these fires is impractical the decisions were made to abandon the towns.  There was no real risk in putting the fire out, it just isn't practically possible to do so.  The risk in letting the fire continue to burn was abated by evacuating the town permanently.  Burning coal does not effect the production of new coal (that takes a long time on geological time scales) -- burning coal is a net reduction in coal mass, not a gain.

As an aside, the other problem with cloud seeding, specifically with the Centralia, PA case, is legal.  Pennsylvania farmers were so concerned about the possibility of cloud seeding elswehere in the state depriving them of needed rainfall that in 1968 the PA state legislature passed "WEATHER MODIFICATION - REGULATING RAIN MAKING. Act of Jan. 19, (1968) 1967, P.L. 1024, No. 449" which means you'll need to convince the state to issue a license for your activities, which is not likely to be granted.


How does the act of producing animal feed from the leftovers of a process result in a negative carbon footprint? According to this source, it is claimed that (emphasis is mine)

At Arbikie Distillery in Angus, scientists claim to have developed the world's first "climate positive" gin. By using garden peas instead of wheat, the company has eliminated the need for carbon-intensive fertilizers and can produce animal feed from the leftover material. Taken together, this gives the drink a negative carbon footprint, Arbikie says.

I am having trouble understanding how the act of producing animal feed from the leftovers of a process results in a negative carbon footprint.

Its only marketing, not a science equation... It's saying that their gin gives back CO$_2$ from other gin which uses fertiliser, and saves a bit of crop space for livestock food. Overall, any crops tend deprive a region of carbon sinks and biodiversity.
Cropland has reduced the worlds total CO$_2$ sinks by 50+ percent, so using 1 crop for 2 things saves CO$_2$ sinks, fertilizer and fossil fuel. Animal stock is intrinsically a climate change accelerator too, especially mammal stocks, which occupy forest space and produce methane and use forest space for hay bales and food crops. Saving surplus foods slightly reduces their impact, its reduces the CO$_2$ budget compared to non surplus feeds.