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https://github.com/mankoff/sankey
Sankey diagram for ice sheet mass flow
https://github.com/mankoff/sankey
antarctica greenland mass sankey tikz
Last synced: 11 days ago
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Sankey diagram for ice sheet mass flow
- Host: GitHub
- URL: https://github.com/mankoff/sankey
- Owner: mankoff
- Created: 2024-04-10T04:30:28.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-06T12:54:35.000Z (5 months ago)
- Last Synced: 2024-06-11T17:34:22.756Z (5 months ago)
- Topics: antarctica, greenland, mass, sankey, tikz
- Language: TeX
- Homepage:
- Size: 40.4 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 13
-
Metadata Files:
- Readme: README.org
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README
#+PROPERTY: header-args:bash+ :session *sankey-shell*
#+PROPERTY: header-args:jupyter-python+ :dir (file-name-directory buffer-file-name) :session foo
* On Ice Sheet Mass Flow
* Table of contents :toc_3:noexport:
- [[#on-ice-sheet-mass-flow][On Ice Sheet Mass Flow]]
- [[#introduction][Introduction]]
- [[#abstract--methods][Abstract & Methods]]
- [[#results][Results]]
- [[#implementation][Implementation]]
- [[#greenland][Greenland]]
- [[#reset][Reset]]
- [[#discharge][Discharge]]
- [[#basal-melt][Basal melt]]
- [[#gz-retreat][GZ retreat]]
- [[#smb][SMB]]
- [[#discharge-1][Discharge]]
- [[#antarctica][Antarctica]]
- [[#export-tables-to-csv][Export tables to CSV]]
- [[#reset-1][Reset]]
- [[#masks-east-west-peninsula-islands-grounded-and-shelves][Masks: East, West, Peninsula, Islands, Grounded and Shelves]]
- [[#smb-mar][SMB (MAR)]]
- [[#basal-melt-1][Basal melt]]
- [[#antarctic-ice-shelves][Antarctic Ice shelves]]
- [[#shelf-freezemelt][Shelf freeze/melt]]
- [[#misc][Misc]]
- [[#export-tables-to-csvs][Export tables to CSVs]]
- [[#convert-pdfs-to-png][Convert PDFs to PNG]]
* Introduction
Sankey diagrams for mass flow in Greenland and Antarctica.
* Abstract & Methods
See [[./ms.tex]]
* Results
#+BEGIN_QUOTE
[!NOTE]
All figures are width-proportional within and between each other.
#+END_QUOTE
#+CALL: pdfs2png()
#+ATTR_ORG: :width 800px
[[./fig_aq_gl.png]]
#+ATTR_ORG: :width 800px
[[./fig_aq_parts.png]]
* Implementation
** Greenland
#+NAME: gl_baseline
| Code | Term | Value | I/O | Period | Source | Comment |
|--------+------------------------+--------+-----+-----------+------------------------------------+-----------------------------------|
| RF | Rainfall | 45 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 10 | I | 2000-2019 | fettweis_2020 | |
| SF | Snowfall | 685 | I | 2000-2019 | fettweis_2020 | |
| RFZ | Refreezing | 195 | IO | 2000-2019 | fettweis_2020 | Melt + rainfall - runoff |
| EV | Evaporation | 10 | O | 2000-2019 | fettweis_2020 | |
| RU | Runoff | 440 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 20 | O | steady | karlsson_2021 | |
| DYN | Discharge | 475-25 | - | 2000-2019 | mankoff_2021_solid,kochtitzky_2023 | Submarine melting + calving - SMB |
| SUB | Submarine melting | 225 | O | | enderlin_2013 | 50 % of discharge |
| SUBF | Freeze-on | 0 | I | | | None in Greenland |
| ICE | Calving | 225 | O | | enderlin_2013 | 50 % of discharge |
| GZRET | Grounding line retreat | 5 | O | | | Estimate |
| FRLOSS | Frontal retreat | 50 | O | 2000-2020 | kochtitzky_2023 | |
| FRGAIN | Frontal advance | 0 | O | | | None in Greenland |
| SU | Sublimation | 60 | O | 2000-2019 | fettweis_2020 | |
| DD | Mass loss | | - | | Derived | sum(O) - sum(I) |
| MG | Mass gain | | - | | Derived | sum(I) - sum(O) |
#+CAPTION: Greenland mass flow terms, values, and metadata
#+CALL: orgtbl2csv(tbl="gl_baseline")
#+RESULTS:
: Exported gl_baseline to ./dat/gl_baseline.csv
*** Reset
#+BEGIN_SRC bash :exports both :results verbatim
trash G_GL
[[ -e ./G_GL ]] || grass -e -c EPSG:3413 ./G_GL
#+END_SRC
*** Discharge
#+BEGIN_SRC bash :exports both :results verbatim
cat ~/data/Mankoff_2021/708/MB_SMB_D_BMB_ann.csv \
| cut -d, -f1,6 \
| sed -e '1,/^1999/d' \
| sed '/^2019/q' \
| datamash -t, mean 1,2
#+END_SRC
#+RESULTS:
: 2009.5,476.44290795
: org_babel_sh_eoe
Then, subtract 25 from 475 based on citet:kochtitzky_2023 who report, in Section 3.3, 17 +- 6.8 and 14.5 +- 5.8 but that "[b]ecause our fluxgates were typically located tens to hundreds of meters lower than those in the similar studies (King et al., 2018; Mankoff et al., 2020), the melt correction for these studies would be higher than values presented herein, although it is beyond the scope of the current study to determine what those values would be."
*** Basal melt
+ 21 Gt/yr from Karlsson (2021) http://doi.org/10.1038/s41467-021-23739-z
+ Assume steady state
*** TODO GZ retreat
From Millan (2022) http://doi.org/10.5194/tc-16-3021-2022
+ Gz retreat is ~0.13 km/yr (Fig. 3a)
+ Ice velocity is ~1200 m/yr (Fig. 3b) (not needed)
+ 20 km wide
Rates are higher per Ciraci (2023) http://doi.org/10.1073/pnas.2220924120, but
+ Ice surface close to flotation near GZ, and shelf is ~500 m thick, so estimate 600 m ice.
Therefore, gz retreat in Gt/year is width * thick * retreat rate * density
#+BEGIN_SRC bash :exports both :results verbatim
frink "0.13 km/yr * 20 km * 600 m * 917 kg/m^3 -> Gt/yr"
#+END_SRC
#+RESULTS:
: 1.43052
Assume similar from other ice shelves too, for a total of ~5 Gt/yr GZ retreat in Greenland.
*** SMB
#+BEGIN_SRC bash :exports both :results verbatim
g.mapset -c MAR
ncdump -v TIME dat/MARv3.12-GRD-15km-annual.nc4 # 20-39 = 2000-2019
ncra --overwrite -d TIME,20,39 dat/MARv3.12-GRD-15km-annual.nc4 tmp/MAR_GL.nc
ncdump -v X10_110 tmp/MAR_GL.nc # 101
ncdump -v Y20_200 tmp/MAR_GL.nc # 181
g.region w=$(( -645000 - 7500 )) e=$(( 855000 + 7500 )) s=$(( -3357928 - 7500 )) n=$((-657928 + 7500 )) res=15000 -p
var=SF # debug
for var in SF RF RU SU ME SMB EVA CON DEP SUB MSK AREA; do
r.in.gdal -o input=NetCDF:tmp/MAR_GL.nc:${var} output=${var}
r.region -c map=${var}
done
r.mapcalc "GL_ice_all = (MSK > 50) & ((x()-y()) > 520000)" # Limit to ice and remove Canada
r.clump input=GL_ice output=clumps --o
main_clump=$(r.stats -c -n clumps sort=desc | head -n2 | tail -n1 | cut -d" " -f1)
r.mapcalc "GL_ice = if(clumps == ${main_clump}, 1, null())"
r.mask raster=GL_ice --o
# scale
## units are mm.w.eq. per grid cell. Grid cell areas are in km^2
## + mm.w.eq. -> m w.eq.: /1E3
## + m w.eq -> kg: *1E3
## + area in km^2 -> m^2: *1E3*1E3
## + kg -> Gt: /1E12
# ds = ds/1E3 * 1E3 * ds['AREA']*1E3*1E3 / 1E12
for var in SF RF RU SU ME SMB EVA CON DEP SUB; do
r.mapcalc "${var} = (${var}/1000) * 1000 * (AREA * 1000*1000) / exp(10,12)"
done
r.mask -r
r.mapcalc "RFZ = ME + RF - RU"
#+END_SRC
#+BEGIN_SRC bash :exports both :results verbatim
for var in SF RF RU ME SMB EVA CON DEP SUB RFZ; do
echo ${var} $(r.univar -g ${var} | grep sum)
done
#+END_SRC
#+RESULTS:
#+begin_example
SF sum=686.768815213334
RF sum=45.5535346610575
RU sum=440.665680238757
ME sum=589.542715610605
SMB sum=235.536411205988
EVA sum=7.9188290228966
CON sum=2.15906279235185
DEP sum=12.2697684982692
SUB sum=61.8983408836194
RFZ sum=194.430570032905
#+end_example
*** Discharge
#+BEGIN_SRC jupyter-python :exports both
import pandas as pd
df = pd.read_csv('/home/kdm/data/Mankoff_2020/ice/GIS_D.csv', index_col=0, parse_dates=True)
df = df['2000-01-01':'2019-12-31']
df.resample('YS').mean().mean().round().astype(int).values[0]
#+END_SRC
#+RESULTS:
: 487
** Antarctica
#+NAME: aq_baseline
| Code | Term | Value | I/O | Period | Source | Comment |
|--------+------------------------+--------------------------+-----+----------------------+-------------------------------------+-----------------|
| RF | Rainfall | 5 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 75 | I | 2000-2019 | fettweis_2020 | |
| SF | Snowfall | 2750 | I | 2000-2019 | fettweis_2020 | |
| RFZ | Refreezing | 105 | IO | 2000-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2000-2019 | fettweis_2020 | |
| RU | Runoff | 10 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 70 | O | - | van-liefferinge_2013 | |
| DYN | Discharge | 1335+1350+(2275-75-1840) | - | 1997-2021; 1999-2017 | Sum of SUB + ICE | See caption |
| SUB | Submarine melting | 1335 | O | 2000-2017 | paolo_2023 | |
| SUBF | Freeze-on | 355 | I | 2000-2017 | paolo_2023 | |
| ICE | Calving | 1350+(2275-75-1840) | O | 1997-2021; 1999-2017 | davison_2023 + rignot_2019 grounded | See caption |
| GZRET | Grounding line retreat | 50 | O | 1997-2021 | Davison (personal comm.) | |
| FRLOSS | Frontal retreat | 79+122+145-1 | O | 2000-2021 | greene_2022 | |
| FRGAIN | Frontal advance | 181+1+103 | O | 2000-2021 | greene_2022 | |
| SU | Sublimation | 230 | O | 2000-2019 | fettweis_2020 | |
| DD | Drawdown | | - | | Derived | sum(O) - sum(I) |
| MG | Mass gain | | - | | Derived | sum(I) - sum(O) |
#+CAPTION: Antarctic mass flow terms, values, and metadata. Calving represents ice shelf calving (1350 Gt yr^{-1} from citet:davison_2023) plus 435 Gt yr^{-1} which is the difference between 2275 Gt yr^{-1} discharge across all sectors 1999 through 2017 from citet:rignot_2019 and 1840 Gt yr^{-1} ice shelf grounding line discharge from citet:davison_2023 - that is, 435 Gt yr^{-1} is estimated as calving from non-shelf sectors. Grounding line retreat here is largerly unknown. We estimate \(\sim\)50 Gt yr^{-1} in the Amundsen sea sector (Davison, personal communication).
#+NAME: aq_east
| Code | Term | Value | I/O | Period | Source | Comment |
|--------+------------------------+--------------------+-----+----------------------+-------------------------------------+-----------------|
| RF | Rainfall | 5 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 40 | I | 2000-2019 | fettweis_2020 | |
| SF | Snowfall | 1555 | I | 2000-2019 | fettweis_2020 | |
| RFZ | Refreezing | 40 | IO | 2000-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2000-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 45 | O | - | van-liefferinge_2013 | |
| DYN | Discharge | 515+680+(1100-910) | - | 1997-2021; 1999-2017 | Sum of SUB + ICE | See caption |
| SUB | Submarine melting | 515 | O | 2000-2017 | paolo_2023 | |
| SUBF | Freeze-on | 200 | I | 2000-2017 | paolo_2023 | |
| ICE | Calving | 680 + (1100-910) | O | 1997-2021; 1999-2017 | davison_2023 + rignot_2019 grounded | See caption |
| GZRET | Grounding line retreat | 5 | O | 1997-2021 | Davison (personal comm.) | |
| FRLOSS | Frontal retreat | 80 | O | 2000-2021 | greene_2022 | |
| FRGAIN | Frontal advance | 180 | O | 2000-2021 | greene_2022 | |
| SU | Sublimation | 175 | O | 2000-2019 | fettweis_2020 | |
| DD | Drawdown | | - | | Derived | sum(O) - sum(I) |
| MG | Mass gain | | - | | Derived | sum(I) - sum(O) |
#+CAPTION: East Antarctic mass flow terms, values, and metadata. Calving here is ice shelf calving (680 Gt yr^{-1} from citet:davison_2023) plus 190 Gt yr^{-1} which is the difference between 1100 Gt yr^{-1} discharge across all sectors 1999 through 2017 from citet:rignot_2019 and 910 Gt yr^{-1} ice shelf grounding line discharge from citet:davison_2023 - that is, 190 Gt yr^{-1} is estimated as calving from non-shelf sectors.
#+NAME: aq_west
| Code | Term | Value | I/O | Period | Source | Comment |
|--------+------------------------+-----------------------+-----+----------------------+-------------------------------------+-----------------|
| RF | Rainfall | 5 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 30 | I | 2000-2019 | fettweis_2020 | |
| SF | Snowfall | 870 | I | 2000-2019 | fettweis_2020 | |
| RFZ | Refreezing | 15 | IO | 2000-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2000-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 20 | O | - | van-liefferinge_2013 | |
| DYN | Discharge | 665 + 560 + (765-765) | - | 1999-2017 | Sum of SUB + ICE | See caption |
| SUB | Submarine melting | 665 | O | 2000-2017 | paolo_2023 | |
| SUBF | Freeze-on | 145 | I | 2000-2017 | paolo_2023 | |
| ICE | Calving | 560 + (765-765) | O | 1997-2021; 1999-2017 | davison_2023 + rignot_2019 grounded | See caption |
| GZRET | Grounding line retreat | 50 | O | 1997-2021 | Davison (personal comm.) | |
| FRLOSS | Frontal retreat | 145 | O | 2000-2021 | greene_2022 | |
| FRGAIN | Frontal advance | 105 | O | 2000-2021 | greene_2022 | |
| SU | Sublimation | 40 | O | 2000-2019 | fettweis_2020 | |
| DD | Drawdown | | - | | Derived | sum(O) - sum(I) |
| MG | Mass gain | | - | | Derived | sum(I) - sum(O) |
#+CAPTION: West Antarctic mass flow terms, values, and metadata. Calving here is ice shelf calving (560 Gt yr^{-1} from citet:davison_2023) plus 0 Gt yr^{-1} which is the difference between 765 Gt yr^{-1} discharge across all sectors 1999 through 2017 from citet:rignot_2019 and 765 Gt yr^{-1} ice shelf grounding line discharge from citet:davison_2023 - that is, 0 Gt yr^{-1} is estimated as calving from non-shelf sectors.
#+NAME: aq_peninsula
| Code | Term | Value | I/O | Period | Source | Comment |
|--------+------------------------+-----------------------+-----+----------------------+-------------------------------------+-----------------|
| RF | Rainfall | 5 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 5 | I | 2000-2019 | fettweis_2020 | |
| SF | Snowfall | 325 | I | 2000-2019 | fettweis_2020 | |
| RFZ | Refreezing | 50 | IO | 2000-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2000-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 5 | O | - | van-liefferinge_2013 | |
| DYN | Discharge | 155 + 105 + (330-160) | - | 1997-2021; 1999-2017 | Sum of SUB + ICE | See caption |
| SUB | Submarine melting | 155 | O | 2000-2017 | paolo_2023 | |
| SUBF | Freeze-on | 10 | I | 2000-2017 | paolo_2023 | |
| ICE | Calving | 105 + (330 - 160) | O | 1997-2021; 1999-2017 | davison_2023 + rignot_2019 grounded | See caption |
| GZRET | Grounding line retreat | 5 | O | 1997-2021 | Davison (personal comm.) | |
| FRLOSS | Frontal retreat | 120 | O | 2000-2021 | greene_2022 | |
| FRGAIN | Frontal advance | 0 | O | 2000-2021 | greene_2022 | |
| SU | Sublimation | 15 | O | 2000-2019 | fettweis_2020 | |
| DD | Drawdown | | - | | Derived | sum(O) - sum(I) |
| MG | Mass gain | | - | | Derived | sum(I) - sum(O) |
#+CAPTION: Antarctic peninsula mass flow terms, values, and metadata. Calving here is ice shelf calving (105 Gt yr^{-1} from citet:davison_2023) plus 170 Gt yr^{-1} which is the difference between 330 Gt yr^{-1} discharge across all sectors 1999 through 2017 from citet:rignot_2019 and 160 Gt yr^{-1} ice shelf grounding line discharge from citet:davison_2023 - that is, 170 Gt yr^{-1} is estimated as calving from non-shelf sectors.
*** Export tables to CSV
#+CALL: orgtbl2csv(tbl="aq_baseline")
#+RESULTS:
: Exported aq_baseline to ./dat/aq_baseline.csv
#+CALL: orgtbl2csv(tbl="aq_east")
#+RESULTS:
: Exported aq_east to ./dat/aq_east.csv
#+CALL: orgtbl2csv(tbl="aq_west")
#+RESULTS:
: Exported aq_west to ./dat/aq_west.csv
#+CALL: orgtbl2csv(tbl="aq_peninsula")
#+RESULTS:
: Exported aq_peninsula to ./dat/aq_peninsula.csv
*** Reset
#+BEGIN_SRC bash :exports both :results verbatim
trash G_AQ
[[ -e ./G_AQ ]] || grass -e -c EPSG:3031 ./G_AQ
#+END_SRC
*** Masks: East, West, Peninsula, Islands, Grounded and Shelves
#+BEGIN_SRC bash :exports both :results verbatim
grass ./G_AQ/PERMANENT
v.in.ogr input=${DATADIR}/NSIDC/NSIDC-0709.002/1992.02.07/IceBoundaries_Antarctica_v02.shp output=basins
g.region vector=basins res=10000 -pas
v.db.select map=basins|head
v.db.select -c map=basins columns=Regions | sort | uniq # East West Peninsula Islands
v.db.select -c map=basins columns=TYPE | sort | uniq # FL GR IS (float, ground, island)
v.to.rast input=basins output=east use=val val=1 where='(Regions == "East")'
v.to.rast input=basins output=west use=val val=2 where='(Regions == "West")'
v.to.rast input=basins output=peninsula use=val val=3 where='(Regions == "Peninsula")'
r.patch input=east,west,peninsula output=basins
r.category basins separator=":" rules=- << EOF
1:East
2:West
3:Peninsula
EOF
r.colors map=basins color=viridis
#+END_SRC
*** SMB (MAR)
#+BEGIN_SRC bash :exports both :results verbatim
g.mapset -c MAR
ncdump -v TIME dat/MARv3.12-ANT-35km-annual.nc4 # 20-39 = 2000-2019
ncra --overwrite -d TIME,20,39 dat/MARv3.12-ANT-35km-annual.nc4 tmp/MAR_AQ.nc
ncdump -v X tmp/MAR_AQ.nc # 176
ncdump -v Y tmp/MAR_AQ.nc # 148
g.region w=$(( -3010000 - 17500 )) e=$(( 3115000 + 17500 )) s=$(( -2555000 - 17500 )) n=$(( 2590000 + 17500 )) res=35000 -p
var=SF # debug
for var in SF RF RU ME SMB EVA CON DEP SUB MSK AREA; do
r.in.gdal -o input=NetCDF:tmp/MAR_AQ.nc:${var} output=${var}
r.region -c map=${var}
done
# scale
## units are mm.w.eq. per grid cell. Grid cell areas are in km^2
## + mm.w.eq. -> m w.eq.: /1E3
## + m w.eq -> kg: *1E3
## + area in km^2 -> m^2: *1E3*1E3
## + kg -> Gt: /1E12
# ds = ds/1E3 * 1E3 * ds['AREA']*1E3*1E3 / 1E12
for var in SF RF RU ME SMB EVA CON DEP SUB; do
r.mapcalc "${var} = (${var}/1000) * 1000 * (AREA * 1000*1000) / exp(10,12)"
done
r.mapcalc "RFZ = ME + RF - RU"
#+END_SRC
**** Stats
#+BEGIN_SRC bash :exports both :results verbatim
r.mask --o raster=basins@PERMANENT --q maskcats="1 thru 3 10 thru 20" # drop 0 and Islands
for var in SF RF RU ME SMB EVA CON DEP SUB RFZ; do
echo -n "${var}"
r.univar -gt map=${var} zones=basins@PERMANENT | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
r.univar -g ${var} | grep sum
echo "#"; echo "#"
done
r.mask -r --q
#+END_SRC
#+RESULTS:
#+begin_example
SF
East 1555.92838304071
West 868.756236659932
Peninsula 327.008298435155
sum=2751.6929181358
RF
East 1.37427316764175
West 0.67184557194045
Peninsula 4.4182855932415
sum=6.46440433282369
RU
East 3.03921478456715
West 0.036433758652
Peninsula 6.24173336942285
sum=9.317381912642
ME
East 41.8875327525325
West 13.5639532884436
Peninsula 51.8076872767586
sum=107.259173317735
SMB
East 1421.34893771318
West 856.678097752916
Peninsula 314.290356315015
sum=2592.31739178111
EVA
East 1.3076393190111
West 0.4376933850929
Peninsula 1.3900330901803
sum=3.1353657942843
CON
East 0.00461569848685
West 0.00432677288165001
Peninsula 0.0478741559012
sum=0.0568166272697001
DEP
East 42.1006070552508
West 28.4439147061151
Peninsula 6.8402185663563
sum=77.384740327722
SUB
East 174.090628819002
West 40.7804740506949
Peninsula 16.1757877048917
sum=231.046890574587
RFZ
East 40.2225911356072
West 14.199365101732
Peninsula 49.9842395005773
sum=104.406195737917
[Raster MASK present]
#+end_example
*** Basal melt
Van Liefferinge (2013) http://doi.org/10.5194/cp-9-2335-2013
Convert MAT file to XYZ for importing into GRASS
#+BEGIN_SRC jupyter-python :exports both
import scipy as sp
import numpy as np
import pandas as pd
mat = sp.io.loadmat('/home/kdm/data/Van_Liefferinge_2023/Melt_Mean_Std_15exp.mat')
X = mat['X'].flatten() * 1E3 # convert from km to m
Y = mat['Y'].flatten() * 1E3
m = mat['MeanMelt'].flatten() / 10 # cm to mm
e = mat['StdMelt'].flatten() / 10 # cm to mm
melt = pd.DataFrame(np.array([X,Y,m,e]).T, columns=['x','y','melt','err']).dropna()
melt.to_csv('./tmp/melt.csv', header=False, index=False)
melt.head()
#+END_SRC
#+RESULTS:
| | x | y | melt | err |
|--------+-----------+------------+-------------+-------------|
| 148741 | 1.045e+06 | -2.14e+06 | 1e-09 | 1.71243e-25 |
| 149859 | 1.03e+06 | -2.135e+06 | 0.00146608 | 0.000148305 |
| 149860 | 1.035e+06 | -2.135e+06 | 0.000266042 | 0.000389444 |
| 149861 | 1.04e+06 | -2.135e+06 | 1e-09 | 1.71243e-25 |
| 149862 | 1.045e+06 | -2.135e+06 | 0.00045698 | 0.000668948 |
#+BEGIN_SRC bash :exports both :results verbatim
grass ./G_AQ/PERMANENT
g.mapset -c liefferinge_2023
r.in.xyz input=./tmp/melt.csv output=melt sep=, --o
r.in.xyz input=./tmp/melt.csv output=err z=4 sep=, --o
#+END_SRC
#+BEGIN_SRC bash :exports both :results verbatim
echo "All: " $(r.univar -g map=melt | grep sum)
echo "All: " $(r.univar -g map=err | grep sum)
echo ""
r.univar -gt map=melt zones=basins | cut -d"|" -f2,13 | column -s"|" -t
echo ""
r.univar -gt map=err zones=basins | cut -d"|" -f2,13 | column -s"|" -t
#+END_SRC
#+RESULTS:
#+begin_example
All: sum=69.3982306335468
All: sum=20.0261054475124
label sum
East 46.7540492694752
West 18.8528624157926
Peninsula 3.18704264192471
label sum
East 14.784924204035
West 4.90322548927998
Peninsula 0.221183549670513
#+end_example
20/69 % = 28.9855072464
*** Antarctic Ice shelves
**** Submarine melt
+ Davison (2023) http://doi.org/10.1126/sciadv.adi0186
#+BEGIN_SRC jupyter-python :exports both
import pandas as pd
fname = '~/data/Davison_2023/adi0186_table_s2.xlsx'
loc = pd.read_excel(fname, sheet_name='Total mass changes', index_col = 0, usecols = 'B,C,D', skiprows = 4)
loc = loc.drop('Antarctic Ice Shelves')
df = pd.read_excel(fname, sheet_name='Steady-state',
index_col = 0, skiprows = 4, usecols=((1,4)))
df.columns = ['Mass']
df = loc.join(df)
import geopandas as gpd
fname = '~/data/NSIDC/NSIDC-0709.002/1992.02.07/IceBoundaries_Antarctica_v02.shp'
ew = gpd.read_file(fname)
df = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['longitude'],df['latitude']), crs="EPSG:4326")
df = df.to_crs('epsg:3031')
e = ew.to_crs('epsg:3031')
idx = ew.sindex.nearest(df['geometry'], return_all=False)
df['Region'] = ''
for dfidx,ewidx in idx.T:
arr = df.iloc[dfidx].copy(deep=True)
arr['Region'] = ew.iloc[ewidx]['Regions']
df.iloc[dfidx] = arr
df = df.drop(columns=['latitude','longitude'])
df.loc['Total'] = [df['Mass'].sum(), None, 'All']
df[['Mass','Region']].groupby('Region').sum().drop('Islands').round()
#+END_SRC
#+RESULTS:
:RESULTS:
: /tmp/ipykernel_3346806/3471234904.py:32: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
: df.loc['Total'] = [df['Mass'].sum(), None, 'All']
| Region | Mass |
|-----------+---------|
| All | 902.775 |
| East | 392.012 |
| Peninsula | 101.994 |
| West | 408.457 |
:END:
**** Calving
Same as above, different sheet. Reuses variables from above, run that first.
#+BEGIN_SRC jupyter-python :exports both
fname = '~/data/Davison_2023/adi0186_table_s2.xlsx'
df = pd.read_excel(fname, sheet_name='Steady-state',
index_col = 0, skiprows = 4, usecols=((1,6)))
df.columns = ['Mass']
df = loc.join(df)
df = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['longitude'],df['latitude']), crs="EPSG:4326")
df = df.to_crs('epsg:3031')
e = ew.to_crs('epsg:3031')
idx = ew.sindex.nearest(df['geometry'], return_all=False)
df['Region'] = ''
for dfidx,ewidx in idx.T:
arr = df.iloc[dfidx].copy(deep=True)
arr['Region'] = ew.iloc[ewidx]['Regions']
df.iloc[dfidx] = arr
df = df.drop(columns=['latitude','longitude'])
df.loc['Total'] = [df['Mass'].sum(), None, 'All']
df[['Mass','Region']].groupby('Region').sum().drop('Islands').round()
#+END_SRC
#+RESULTS:
:RESULTS:
: /tmp/ipykernel_3346806/353247760.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
: df.loc['Total'] = [df['Mass'].sum(), None, 'All']
| Region | Mass |
|-----------+----------|
| All | 1348.02 |
| East | 681.734 |
| Peninsula | 103.439 |
| West | 561.832 |
:END:
**** Discharge
Same as above, different sheet. Reuses variables from above, run that first.
#+BEGIN_SRC jupyter-python :exports both
fname = '~/data/Davison_2023/adi0186_table_s2.xlsx'
df = pd.read_excel(fname, sheet_name='Steady-state',
index_col = 0, skiprows = 4, usecols=((1,2)))
df.columns = ['Mass']
df = loc.join(df)
df = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['longitude'],df['latitude']), crs="EPSG:4326")
df = df.to_crs('epsg:3031')
e = ew.to_crs('epsg:3031')
idx = ew.sindex.nearest(df['geometry'], return_all=False)
df['Region'] = ''
for dfidx,ewidx in idx.T:
arr = df.iloc[dfidx].copy(deep=True)
arr['Region'] = ew.iloc[ewidx]['Regions']
df.iloc[dfidx] = arr
df = df.drop(columns=['latitude','longitude'])
df.loc['Total'] = [df['Mass'].sum(), None, 'All']
df[['Mass','Region']].groupby('Region').sum().drop('Islands').round()
#+END_SRC
#+RESULTS:
:RESULTS:
: /tmp/ipykernel_3346806/927385710.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
: df.loc['Total'] = [df['Mass'].sum(), None, 'All']
| Region | Mass |
|-----------+----------|
| All | 1838.8 |
| East | 910.573 |
| Peninsula | 159.697 |
| West | 767.324 |
:END:
**** Frontal Retreat
#+BEGIN_QUOTE
[greene_Supplementary_Table_1.xlsx](https://github.com/user-attachments/files/15598602/greene_Supplementary_Table_1.xlsx)
I think the data in the attached spreadsheet from [Greene et al., 2022 ](https://doi.org/10.1038/s41586-022-05037-w) is everything needed for ice-shelf mass-change resulting from frontal advance/retreat, so in Excel `=BI189-O189` gives Antarctica's net retreat from 1997 to 2021. Change the column to adjust the time period.
#+END_QUOTE
BI189 = 24596304.0
BI189 = 2021.2
Q189 = 24597630.0
Q189 = 2000.2
(24596304.0 - 24597630.0) / (2021.2-2000.2) = -63.1428571429
But we need to recreate this in code so we can split by east/west/peninsula
#+BEGIN_SRC jupyter-python :exports both
import pandas as pd
import geopandas as gpd
fname = "~/data/Greene_2022/data/greene_Supplementary_Table_1.xlsx"
df = pd.read_excel(fname, sheet_name='greene_iceshelf_area_and_mass',
index_col = 1, skiprows = 4)
df = df.rename(columns={'Unnamed: 2':'lat',
'Unnamed: 3':'lon'})
# drop uncertainty columns
unc = []
for c in df.columns:
if type(c) == str:
if c[0:8] == 'Unnamed:':
unc.append(c)
df = df.drop(columns = unc)
df = df[['lat','lon',2000.2,2021.2]]
df = df.iloc[1:]
# Remove last two rows
aq = df.loc['Antarctica']
other = df.loc['Other']
df = df.iloc[:-2]
#+END_SRC
#+RESULTS:
#+BEGIN_SRC jupyter-python :exports both
print(df.sum())
print("")
print(aq)
print("")
print(other)
#+END_SRC
#+RESULTS:
#+begin_example
lat -12882.373098
lon 6279.268331
2000.2 682491.281291
2021.2 681213.775349
dtype: object
lat -90
lon every
2000.2 24597630
2021.2 24596304
Name: Antarctica, dtype: object
lat NaN
lon NaN
2000.2 23915136
2021.2 23915090
Name: Other, dtype: object
#+end_example
#+BEGIN_SRC jupyter-python :exports both
shelf = df.sum()
print("All AQ loss: ", (aq[2021.2] - aq[2000.2]) / (2021-2000))
print("Named shelf loss: ", (shelf[2021.2] - shelf[2000.2]) / (2021-2000))
print("Other loss: ", (other[2021.2] - other[2000.2]) / (2021-2000))
print("Named + Other: ", (((other + shelf)[2021.2] - (other + shelf)[2000.2]) / (2021-2000)))
print("Named %: ", 2.19/63.02*100)
#+END_SRC
#+RESULTS:
: All AQ loss: -63.142857142857146
: Named shelf loss: -60.83361628651619
: Other loss: -2.1904761904761907
: Named + Other: -63.02409247699238
: Named %: 3.4750872738813077
#+BEGIN_SRC jupyter-python :exports both
import geopandas as gpd
fname = '~/data/NSIDC/NSIDC-0709.002/1992.02.07/IceBoundaries_Antarctica_v02.shp'
ew = gpd.read_file(fname)
ew.drop(columns=['geometry']).head()
#+END_SRC
#+RESULTS:
| | NAME | Regions | Subregions | TYPE | Asso_Shelf |
|----+----------------+-----------+--------------+--------+----------------|
| 0 | LarsenE | Peninsula | Ipp-J | GR | LarsenE |
| 1 | Dawson_Lambton | East | nan | FL | nan |
| 2 | Academy | East | Jpp-K | GR | Filchner |
| 3 | Brunt_Stancomb | East | K-A | GR | Brunt_Stancomb |
| 4 | Riiser-Larsen | East | K-A | GR | Riiser-Larsen |
#+BEGIN_SRC jupyter-python :exports both
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['lon'],df['lat']), crs="EPSG:4326")
gdf = gdf.to_crs('epsg:3031')
ew = ew.to_crs('epsg:3031')
idx = ew.sindex.nearest(gdf['geometry'], return_all=False)
gdf['Region'] = ''
for gdfidx,ewidx in idx.T:
arr = gdf.iloc[gdfidx].copy(deep=True)
arr['Region'] = ew.iloc[ewidx]['Regions']
gdf.iloc[gdfidx] = arr
gdf.head()
gdf.loc['Total'] = gdf.sum(axis='rows')
gdf.loc['Total', 'Region'] = 'All'
gdf['frontal change'] = (gdf[2021.2] - gdf[2000.2]) / (2021.2-2000.2)
pos = gdf[gdf['frontal change'] > 0]
neg = gdf[gdf['frontal change'] <= 0]
# gdf
print('neg', neg[['Region','frontal change']].groupby('Region').sum().round().abs())
print('')
print('pos', pos[['Region','frontal change']].groupby('Region').sum().round().abs())
print('')
print('all', gdf[['Region','frontal change']].groupby('Region').sum().round().abs())
#+END_SRC
#+RESULTS:
#+begin_example
neg frontal change
Region
All 61.0
East 79.0
Peninsula 122.0
West 145.0
pos frontal change
Region
East 181.0
Peninsula 1.0
West 103.0
all frontal change
Region
All 61.0
East 102.0
Peninsula 121.0
West 42.0
#+end_example
**** GZ retreat
Email from Davison
| Ice Shelf | Mass change due to grounding line migration from 1997 to 2021 (Gt) | Error (Gt) |
| Pine Island | 220 | 40 |
| Thwaites | 230 | 25 |
| Crosson | 200 | 25 |
| Dotson | 420 | 80 |
(220+230+200+420)/(2021-1997) = 44.5833333333
*** Shelf freeze/melt
#+BEGIN_SRC jupyter-python :exports both
import xarray as xr
ds = xr.open_mfdataset("~/data/Paolo_2023/ANT_G1920V01_IceShelfMelt.nc")
ds = ds[['melt','melt_err']].sel({'time':slice('2000-01-01','2017-12-31')}).mean(dim='time')
delayed_obj = ds.to_netcdf('tmp/shelf_melt.nc', compute=False)
from dask.diagnostics import ProgressBar
with ProgressBar():
results = delayed_obj.compute()
print(ds)
#+END_SRC
#+RESULTS:
: [########################################] | 100% Completed | 7.79 s
: Size: 68MB
: Dimensions: (y: 2916, x: 2916)
: Coordinates:
: * x (x) float64 23kB -2.798e+06 -2.796e+06 ... 2.796e+06 2.798e+06
: * y (y) float64 23kB 2.798e+06 2.796e+06 ... -2.796e+06 -2.798e+06
: Data variables:
: melt (y, x) float32 34MB dask.array
: melt_err (y, x) float32 34MB dask.array
#+BEGIN_SRC bash :exports both :results verbatim
g.mapset -c Paolo_2023
ncdump -v x tmp/shelf_melt.nc # 2916x2916
ncdump -v y tmp/shelf_melt.nc
x0=-2798407.5
x1=2798392.5
y0=-2798392.5
y1=2798407.5
g.region w=$(( -2798407 - 960 )) e=$(( 2798392 + 960 )) s=$(( -2798392 - 960 )) n=$(( 2798407 + 960 )) res=1920 -p
r.mapcalc "area = area()"
r.in.gdal -o input=NetCDF:tmp/shelf_melt.nc:melt output=melt
r.in.gdal -o input=NetCDF:tmp/shelf_melt.nc:melt_err output=err
r.region -c map=melt
r.region -c map=err
## + kg/m^2 -> Gt: / 1E12
r.mapcalc "melt = melt * 1000 * area / exp(10,12)" --o
r.mapcalc "err = err * 1000 * area / exp(10,12)" --o
r.mapcalc "melt_on = if(melt > 0, melt, null())"
r.mapcalc "err_on = if(melt > 0, err, null())"
r.mapcalc "melt_off = if(melt < 0, melt, null())"
r.mapcalc "err_off = if(melt < 0, err, null())"
r.colors -ae map=melt color=difference
r.colors -ge map=melt_on color=viridis
r.colors -ge map=melt_off color=viridis
# d.rast melt
# d.rast melt_on
# d.rast melt_off
r.mapcalc "basins = if((basins@PERMANENT == 1) | (basins@PERMANENT == 11), 1, 0)"
r.mapcalc "basins = if((basins@PERMANENT == 2) | (basins@PERMANENT == 12), 2, basins)"
r.mapcalc "basins = if((basins@PERMANENT == 3) | (basins@PERMANENT == 13), 3, basins)"
r.colors map=basins color=viridis
r.category basins separator=":" rules=- << EOF
1:East
2:West
3:Peninsula
EOF
#+END_SRC
**** Stats
#+BEGIN_SRC bash :exports both :results verbatim
echo "NET"
r.univar -gt map=melt zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
r.univar -gt map=err zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
# r.univar -g melt | grep sum
echo ""
echo "FREEZE_ON"
r.univar -gt map=melt_on zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
r.univar -gt map=err_on zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
# r.univar -g melt_on | grep sum
echo ""
echo "MELT_OFF"
r.univar -gt map=melt_off zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
r.univar -gt map=err_off zones=basins | cut -d"|" -f2,13 | column -s"|" -t | sed 's/label.*//'
# r.univar -g melt_off | grep sum
#+END_SRC
#+RESULTS:
#+begin_example
NET
East -314.59815832386
West -516.40649095369
Peninsula -144.781596164836
East 1262.04130311163
West 1500.90827154852
Peninsula 230.826477084729
FREEZE_ON
East 199.82814766386
West 146.110647983701
Peninsula 10.5669662518996
East 462.492239471166
West 576.342637546049
Peninsula 35.4516506207232
MELT_OFF
East -514.426305987718
West -662.517138937384
Peninsula -155.348562416735
East 799.549063640453
West 924.565634002478
Peninsula 195.374826464004
#+end_example
* Misc
** Export tables to CSVs
#+NAME: orgtbl2csv
#+BEGIN_SRC emacs-lisp :var tbl="" :colnames no
(save-excursion
(goto-char (point-min))
(re-search-forward (concat "^#\\+name: " tbl) nil t)
(next-line)
(org-table-export (concat "./dat/" tbl ".csv") "orgtbl-to-csv")
;;(shell-command-to-string (concat "head " tbl ".csv"))
(message (concat "Exported " tbl " to " (concat "./dat/" tbl ".csv")))
)
#+END_SRC
** Convert PDFs to PNG
#+NAME: pdfs2png
#+BEGIN_SRC bash :exports results :results verbatim :results none
convert -density 300 -background white -alpha remove -trim -gravity center -annotate -100+75 'Greenland' gl_baseline.pdf tmp/gl.png
convert -density 300 -background white -alpha remove -trim -gravity center -annotate -100+50 'Antarctica' aq_baseline.pdf tmp/aq.png
convert -density 300 -background white -alpha remove -trim -gravity center -annotate -100+50 'East' aq_east.pdf ./tmp/aqe.png
convert -density 300 -background white -alpha remove -trim -gravity center -annotate -100+75 'West' aq_west.pdf ./tmp/aqw.png
convert -density 300 -background white -alpha remove -trim -gravity center -annotate -100+60 'Peninsula' aq_peninsula.pdf ./tmp/aqp.png
convert -density 300 -background transparent -alpha remove legend.svg ./tmp/legend.png
composite -gravity center -geometry '100%x100%+200-90' tmp/legend.png tmp/aq.png tmp/aq_legend.png
convert -gravity center -append tmp/{gl,aq_legend}.png ./fig_aq_gl.png
convert -gravity center -append tmp/{aqe,aqw,aqp}.png ./fig_aq_parts.png
#+END_SRC