- On Ice Sheet Mass Flow
- Introduction
- Abstract & Methods
- Results
- Processes and flow
- Implementation
- Misc
Sankey diagrams for mass flow in Greenland and Antarctica.
See ./ms.tex
Note
All figures are width-proportional within and between each other.
| ID | Term | Value | IO | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 40 | Ig | 2010-2019 | fettweis_2020 | |
| CD | Condensation | 5 | Ig | 2010-2019 | fettweis_2020 | |
| DP | Deposition | 10 | Ig | 2010-2019 | fettweis_2020 | |
| SF | Snowfall | 680 | Ig | 2010-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | Og | 2010-2019 | fettweis_2020 | |
| RU | Runoff | 435 | Og | 2010-2019 | fettweis_2020 | |
| SU | Sublimation | 60 | Og | 2010-2019 | fettweis_2020 | |
| RFZ | Refreezing | 200 | - | 2010-2019 | fettweis_2020 | Melt + rainfall - runoff |
| BMB | Grounded basal melting | 20 | Og | steady | karlsson_2021 | Grounded basal mass balance |
| DISCH | Discharge | 485-20+5 | Og | 2010-2019 | mankoff_2020_solid,kochtitzky_2023,bollen_2023 | Discharge (less SMB below gates) = Frontal melting + calving + peripheral |
| CALV | Calving | 235 | Oo | enderlin_2013 | 50 % of discharge | |
| FRONTMELT | Frontal melting | 235 | Oo | enderlin_2013 | 50 % of discharge | |
| SHELFMELT | Sub-shelf melting | 25 | Oo | 2013-2022 | wang_2024 | Assume 10 Gt is steady state in FRONTMELT. Remaining 25 Gt is net loss |
| SHELFFREEZE | Sub-shelf freeze-on | 5 | Io | 2013-2022 | wang_2024 | |
| GZRET | Grounding line retreat | 5 | Og | Estimate | ||
| FRONTRET | Frontal retreat | 50 | Oo | 2010-2020 | kochtitzky_2023 | |
| FRONTADV | Frontal advance | 0 | Oo | 2010-2020 | kochtitzky_2023 | None in Greenland |
Exported gl_baseline to ./dat/gl_baseline.csv
trash G_GL
[[ -e ./G_GL ]] || grass -e -c EPSG:3413 ./G_GL!date
fname="https://thredds.geus.dk/thredds/fileServer/MassBalance/MB_SMB_D_BMB.csv"
df = pd.read_csv(fname, index_col=0, parse_dates=True)[['D','D_err']]
df = df\
.resample('1D').interpolate().resample('YS').sum()
print("2010s")
print(df['2010-01-01':'2019-12-31'].mean())
Wed Dec 18 07:11:05 AM EST 2024 2010s D 485.373414 D_err 44.951388 dtype: float64
Then, subtract 15 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.”
grass ./G_GL/PERMANENT
g.mapset -c Bollen_2023
cat "${DATADIR}/Bollen_2023/GreenlandGIC_discharge_timeseries - Ellyn Enderlin.csv" \
| cut -d, -f1-3 \
| v.in.ascii input=- output=bollen_2023 separator=, skip=1 x=2 y=3 z=1import pandas as pd
data_root='/home/kdm/data'
path='Bollen_2023'
fname='GreenlandGIC_discharge_timeseries - Ellyn Enderlin.csv'
df = pd.read_csv(f"{data_root}/{path}/{fname}", index_col=0, header=[0])
df = df.sum(axis='rows')
df = df / 1E9 # per email from Ellyn, units are m^3/year. Convert to Gt.
df = df['2010':'2018']
df.mean()
5.209345977852399
- 21 Gt/yr from Karlsson (2021) http://doi.org/10.1038/s41467-021-23739-z
- Assume steady state
From Millan (2022) http://doi.org/10.5194/tc-16-3021-2022
- Gz retreat is ~0.13 km/yr (Fig. 3a)
- Ice velocity is ~1200 m/yr (Fig. 3b) (not needed)
- 20 km wide
Rates are higher per Ciraci (2023) http://doi.org/10.1073/pnas.2220924120, but
- Ice surface close to flotation near GZ, and shelf is ~500 m thick, so estimate 600 m ice.
Therefore, gz retreat in Gt/year is width * thick * retreat rate * density
frink "0.13 km/yr * 20 km * 600 m * 917 kg/m^3 -> Gt/yr"1.43052
Assume similar from other ice shelves too, for a total of ~5 Gt/yr GZ retreat in Greenland.
g.mapset -c MAR
ncdump -v TIME dat/MARv3.12-GRD-15km-annual.nc4 # 30-39 = 2010-2019
ncra --overwrite -d TIME,30,39 dat/MARv3.12-GRD-15km-annual.nc4 tmp/MAR_GL.nc
ncdump -v X10_110 tmp/MAR_GL.nc # 101
ncdump -v Y20_200 tmp/MAR_GL.nc # 181
g.region w=$(( -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 == ${main_clump}, 1, null())"
# r.mask raster=GL_ice --o
r.mapcalc "MASK = if(GL_ice_all == 1)" --o
# if only X % of a cell is ice, scale by that.
r.mapcalc "scale_mask = (GL_ice_all * MSK) / 100"
# scale
## units are mm.w.eq. per grid cell. Grid cell areas are in km^2
## + mm.w.eq. -> m w.eq.: /1E3
## + m w.eq -> kg: *1E3
## + area in km^2 -> m^2: *1E3*1E3
## + kg -> Gt: /1E12
# ds = ds/1E3 * 1E3 * ds['AREA']*1E3*1E3 / 1E12
for var in SF RF RU SU ME SMB EVA CON DEP SUB; do
r.mapcalc "${var} = (${var}/1000) * 1000 * (AREA * 1000*1000) * scale_mask / exp(10,12)"
done
r.mask -r
r.mapcalc "RFZ = ME + RF - RU"for var in SF RF RU ME SMB EVA CON DEP SUB RFZ; do
echo ${var} $(r.univar -g ${var} | grep sum)
done�[?2004lSF sum=678.472341306034 RF sum=41.0073369748482 RU sum=433.411271134275 ME sum=594.819117205514 SMB sum=232.245706856329 EVA sum=7.43645901936729 CON sum=2.02922271273767 DEP sum=12.3770587084991 SUB sum=60.0712550947222 RFZ sum=202.41518304609
grass ./G_GL/PERMANENT
g.mapset -c Wang_2024
tif_list=$(find ~/data/Wang_2024 -name "????.tif")
t=$(echo $tif_list | tr ' ' '\n' | head -n1) # debug
for t in ${tif_list}; do
dirname=$(basename $(dirname ${t}))
fname=$(basename ${t})
fname=${fname%.*}
tname=g_${dirname}_${fname} # add g_ because "79N" is not a valid name
r.in.gdal input=${t} output=${tname}
done
g.region raster=$(g.list type=raster sep=,) -pa
r.series input=$(g.list type=raster sep=,) output=melt method='average'
r.colors -a map=melt color=viridis
r.mapcalc "area = area()"
## Melt data is m/year
## Multiply by area to get m/m^2 or grams, then 1000 to get kg
r.mapcalc "melt = melt * 1000 * area / exp(10,12)" --o
r.mapcalc "melt_on = if(melt > 0, melt, null())"
r.mapcalc "freeze_on = if(melt < 0, melt, null())"echo "NET"
r.univar -gt map=melt | cut -d"|" -f11
echo ""
echo "FREEZE_ON"
r.univar -gt map=freeze_on | cut -d"|" -f11
echo ""
echo "MELT_OFF"
r.univar -gt map=melt_on | cut -d"|" -f11�[?2004lNET �[?2004lsum 33.4127947245078 �[?2004l �[?2004lFREEZE_ON ?2004lsum -2.68199438110646 �[?2004l �[?2004lMELT_OFF �[?2004lsum 36.094789105614
import xarray as xr
ds = xr.open_dataset("~/data/Dohne_2023/GIS_GMB_grid.nc")
ds['dm'] = ds['dm'] * ds['area']
ds = ds.sel({'time':slice('2010-01-01','2019-12-31')})
ds = data=ds['dm'].to_dataset()
ds = ds['dm'].sum(dim=['x','y'])/1E12
ds = ds - ds.values[0]
_ = ds.plot()
ds = ds.resample({'time':'YS'}).mean()
ds = ds.diff(dim='time')
print(ds.mean())
<xarray.DataArray 'dm' ()> Size: 8B array(-250.12027707)
import pandas as pd
from datetime import datetime, timedelta
df = pd.read_csv("~/data/GRACE/greenland_mass_200204_202409.txt",
comment="H", parse_dates=True, sep="\s+", header=None,
names=['year','mass','err'])
# Function to convert year.frac to ISO format (YYYY-MM-DD)
def year_frac_to_iso(year_frac):
year = int(year_frac)
frac = year_frac - year
start_of_year = datetime(year, 1, 1)
days_in_year = (datetime(year + 1, 1, 1) - start_of_year).days
date = start_of_year + timedelta(days=frac * days_in_year)
return pd.to_datetime(date.strftime('%Y-%m-%d'))
# Apply the conversion to the 'Year' column
df['date'] = df['year'].apply(year_frac_to_iso)
df = df.drop(columns=['year'])
df = df.set_index('date')
df = df['mass']
# df.resample('D').mean().interpolate()
df = df['2010-01-01':'2019-12-31']
df = df - df.max()
# df.head()
_ = df.plot()
print(df.resample('YS').mean().diff().mean())
-264.96212962962966
!date
fname="https://thredds.geus.dk/thredds/fileServer/MassBalance/MB_SMB_D_BMB.csv"
df = pd.read_csv(fname, index_col=0, parse_dates=True)[['MB','MB_err']]
df = df\
.resample('1D').interpolate().resample('YS').sum()
print("2010s")
print(df['2010-01-01':'2019-12-31'].mean())
Fri Dec 20 09:56:38 AM EST 2024 2010s MB -246.172157 MB_err 94.196209 dtype: float64
MB per GRACE is -140 https://svs.gsfc.nasa.gov/31166 but long trend. Not sure what recent trend is.
SMB w/ Islands (57) but w/o shelves = 2231 from MAR 2010-2019 D from Rignot: 2231 + 77 (islands) = 2308. This includes non-shelf discharge which is 330 Gt/year MB = SMB - D => 2232 - 2308 = -76. Missing -70 BMB = -146. Good match!
D-shelf from Davison: 1935.082208. Add 77 (Rignot Islands) + 331 (Rignot non-shelf D) = 1935+77+331 = 2343 MB = SMB - D => 2232 - 2343 = -111. Missing -70 BMB = -181. Decent match.
| ID | Term | Value | IO | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 5 | I | 2010-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2010-2019 | fettweis_2020 | |
| DP | Deposition | 80 | I | 2010-2019 | fettweis_2020 | |
| SF | Snowfall | 2775 | I | 2010-2019 | fettweis_2020 | |
| RFZ | Refreezing | 105 | - | 2010-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2010-2019 | fettweis_2020 | |
| RU | Runoff | 10 | O | 2010-2019 | fettweis_2020 | |
| SU | Sublimation | 230 | O | 2010-2019 | fettweis_2020 | |
| BMB | Grounded basal melting | 70 | O | - | van-liefferinge_2013 | |
| DISCH | Discharge | 1935+331+77+2 | O | 2008-2019 | davison_2023 (to shelves) + rignot_2019 (grounded + islands) | |
| CALV | Calving | 1411+331+77+1 | O | 2010-2019 | greene_2022 + rignot_2019 discharge (grounded + islands) | |
| FRONTMELT | Frontal melting | 0 | O | |||
| SHELFMELT | Sub-shelf melting | 1380 | O | 2010-2017 | paolo_2023 | |
| SHELFFREEZE | Sub-shelf Freeze-on | 405 | I | 2010-2017 | paolo_2023 | |
| GZRET | Grounding line retreat | 45 | O | 1997-2021 | davison_2023 (only Pine Island, Thwaites, Crosson, and Dotson) | |
| FRONTRET | Frontal retreat | 400 | O | 2010-2021 | greene_2022 | |
| FRONTADV | Frontal advance | 195 | O | 2010-2021 | greene_2022 |
| ID | Term | Value | IO | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 5 | I | 2010-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2010-2019 | fettweis_2020 | |
| DP | Deposition | 40 | I | 2010-2019 | fettweis_2020 | |
| SF | Snowfall | 1535+20 | I | 2010-2019 | fettweis_2020 | 1535 + East AQ Islands |
| RFZ | Refreezing | 40 | IO | 2010-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2010-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2010-2019 | fettweis_2020 | |
| SU | Sublimation | 170 | O | 2010-2019 | fettweis_2020 | |
| BMB | Grounded basal melting | 45 | O | - | van-liefferinge_2013 | |
| DISCH | Discharge | 924+177+23+1 | - | 2008-2019 | davison_2023 (to shelves) + rignot_2019 (grounded + islands) | |
| CALV | Calving | 694+177-1 | O | 2010-2019 | greene_2022 + rignot_2019 discharge (grounded + islands) | |
| FRONTMELT | Submarine melting | 0 | ||||
| SHELFMELT | Sub-shelf melting | 515 | O | 2010-2017 | paolo_2023 | |
| SHELFFREEZE | Sub-shelf freeze-on | 200 | I | 2010-2017 | paolo_2023 | |
| GZRET | Grounding line retreat | 5 | O | 1997-2021 | davison_2023 | |
| FRONTRET | Frontal retreat | 70 | O | 2010-2019 | greene_2022 | |
| FRONTADV | Frontal advance | 190 | O | 2010-2019 | greene_2022 |
| ID | Term | Value | IO | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 5 | I | 2010-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2010-2019 | fettweis_2020 | |
| DP | Deposition | 30 | I | 2010-2019 | fettweis_2020 | |
| SF | Snowfall | 865 +30 | I | 2010-2019 | fettweis_2020 | 865 + West AQ Islands |
| RFZ | Refreezing | 15 | IO | 2010-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2010-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2010-2019 | fettweis_2020 | |
| SU | Sublimation | 40 | O | 2010-2019 | fettweis_2020 | |
| BMB | Grounded basal melting | 20 | O | - | van-liefferinge_2013 | |
| DISCH | Discharge | 857+23+45 | - | 2008-2019 | davison_2023 (to shelves) + rignot_2019 (grounded + islands) | |
| CALV | Calving | 567+23 | O | 2010-2019 | greene_2022 + rignot_2019 discharge (grounded + islands) | |
| FRONTMELT | Frontal melting | 0 | ||||
| SHELFMELT | Sub-shelf melting | 665 | O | 2010-2017 | paolo_2023 | |
| SHELFFREEZE | Sub-shelf freeze-on | 145 | I | 2010-2017 | paolo_2023 | |
| GZRET | Grounding line retreat | 50 | O | 1997-2021 | davison_2023 | |
| FRONTRET | Frontal retreat | 205 | O | 2010-2019 | greene_2022 | |
| FRONTADV | Frontal advance | 5 | O | 2010-2019 | greene_2022 |
| ID | Term | Value | IO | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 5 | I | 2010-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2010-2019 | fettweis_2020 | |
| DP | Deposition | 5 | I | 2010-2019 | fettweis_2020 | |
| SF | Snowfall | 320+5 | I | 2010-2019 | fettweis_2020 | 320 + Peninsula Islands |
| RFZ | Refreezing | 45 | IO | 2010-2019 | fettweis_2020 | |
| EV | Evaporation | 5 | O | 2010-2019 | fettweis_2020 | |
| RU | Runoff | 5 | O | 2010-2019 | fettweis_2020 | |
| SU | Sublimation | 15 | O | 2010-2019 | fettweis_2020 | |
| BMB | Grounded basal melting | 5 | O | - | van-liefferinge_2013 | |
| DISCH | Discharge | 153+131+9-1 | - | 2008-0219 | davison_2023 (to shelves) + rignot_2019 (grounded + islands) | |
| CALV | Calving | 104+131 | O | 2010-2019 | greene_2022 + rignot_2019 discharge (grounded + islands) | |
| FRONTMELT | Frontal melting | 0 | ||||
| SHELFMELT | Sub-shelf melting | 155 | O | 2010-2017 | paolo_2023 | |
| SHELFFREEZE | Sub-shelf freeze-on | 10 | I | 2010-2017 | paolo_2023 | |
| GZRET | Grounding line retreat | 5 | O | 1997-2021 | davison_2023 | |
| FRONTRET | Frontal retreat | 125 | O | 2010-2019 | greene_2022 | |
| FRONTADV | Frontal advance | 5 | O | 2010-2019 | greene_2022 |
Exported aq_baseline to ./dat/aq_baseline.csv
Exported aq_east to ./dat/aq_east.csv
Exported aq_west to ./dat/aq_west.csv
Exported aq_peninsula to ./dat/aq_peninsula.csv
trash G_AQ
[[ -e ./G_AQ ]] || grass -e -c EPSG:3031 ./G_AQgrass ./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")'
v.to.rast input=basins output=islands use=val val=4 where='(Regions == "Islands")'
r.patch input=east,west,peninsula,islands output=basins
r.category basins separator=":" rules=- << EOF
1:East
2:West
3:Peninsula
4:Islands
EOF
r.colors map=basins color=viridis
v.to.rast input=basins output=ground use=val val=1 where='(TYPE == "GR") or (TYPE == "IS")'
v.to.rast input=basins output=ground_noisland use=val val=1 where='(TYPE == "GR")'Rignot 2019 provides discharge for Islands, but not by region. Here, determine island area per region. This assumes all islands have the same discharge per unit area.
r.patch input=east,west,peninsula output=main_ice
r.colors map=main_ice color=viridis
r.grow.distance input=main_ice value=main_ice_grow
r.mapcalc "islands_near = if(islands, main_ice_grow)"r.stats --q -a input=islands_near�[?2004l1-1.007843 41700000000.000000 1.996078-2.003922 80300000000.000000 2.992157-3 17400000000.000000 * 23004600000000.000000
Area = 417 + 803 + 174 = 1394 East = 417 / 1394 % = 29.9139167862 = 30 West = 803 / 1394 % = 57.6040172166 = 58 Peninsula = 174 / 1394 % = 12.4820659971 = 12
- Discharge is “grounded discharge”
- Input to ice shelves where ice shelves exist
- Calving (similar to Greenlandic discharge) where ice shelves do not exist.
import pandas as pd
df = pd.read_excel("~/data/Rignot_2019/pnas.1812883116.sd01.xlsx", index_col=0)
##############################################################################
###
### cleanup
###
df = df.loc[df.index.dropna()]
for i in [0,0,0]: # drop Excel rows 2,3,4
df = df.drop(index=df.index[i])
# Drop super-shelves and rename indented sub-shelves
super_shelf = ["LarsenB", "Wordie", "Ronne", "Ross West", "Ross East", "Amery_Ice_Shelf", "Filchner", "AP", "WAIS", "EAIS", "TOTAL SURVEYED"]
df = df.drop(index=super_shelf)
for i in df.index:
if i[0] == ' ': df = df.rename(index={i: i.strip()})
for c in df.columns: # Drop extra columns
if 'Unnamed' in str(c):
df = df.drop(columns=c)
df = df.drop(columns=["Basin.1", "σ SMB", "σ D", "D type"]) # Drop unused columns
##############################################################################
# Green color = no ice shelf
noshelf = ["West_Graham_Land", "Eastern_Graham_Land", "Hektoria_Headland", "Evans_Headland", "Drygalski_Headland", "LarsenA", "Rydberg_Peninsula", "Zonda_Eureka", "Cape_Jeremy", "Wilkins_George_VI", "Wilkins_Island", "Thomson", "Fox", "Cooke", "Walgreen_Coast", "Lucchitta_Velasco", "Jackson-Perkins", "Frostman-Lord-Shuman-Anandakri", "Shirases_Coast", "Saunders_Coast", "Ross_East1", "Ross_East2", "Ross_East3", "Ross_East4", "Ross_East5", "Dry_Valleys", "Icebreaker-Fitzgerald", "Victoria_Land", "Oates_Coast", "Wilkes_Land", "Adelie_Coast", "Sabrina_Coast", "Clarie_Coast", "Law_Dome", "Budd_Coast", "Knox_Coast", "Ingrid_Christensen_Coast", "Wilhelm_II_Coast", "Enderby_Land", "Prince_Olav_Coast", "Mawson_Coast"]
df['shelf'] = 1
df.loc[noshelf, 'shelf'] = 0
# Sum numeric columns
df.loc['Sum'] = np.nan
for c in df.columns: # convert to numbers
try: df[c] = pd.to_numeric(df[c])
except: df.loc['Sum',c] = 'All'
cols = df.select_dtypes(include=[np.number]).columns.drop('shelf')
df.loc['Sum', cols] = df[cols].sum(axis='rows')
cols = df.columns[0:10].to_list()
cols.insert(3,'shelf')
df[cols].tail(10)
| Glacier name | Basin | Region | Subregion | shelf | SMB | D | 1979 | 1980 | 1981 | 1982 | 1983 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Stancomb_Wills | K-A | East | Stancomb_Wills_Ice_Shelf | 1 | 22.03 | 21.39 | 25.261 | 24.7291 | 24.1972 | 23.6653 | 23.1333 |
| Princess_Martha | K-A | East | Princess_Martha_Coast | 1 | 0.21 | 0.21 | 0.21 | 0.21 | 0.21 | 0.21 | 0.21 |
| Coats_Coast | K-A | East | Coats_Coast | 1 | 6.43 | 6.43 | 6.43 | 6.43 | 6.43 | 6.43 | 6.43 |
| Academy | J”-K | East | Filchner_Ice_Shelf | 1 | 24.27 | 24.1 | 24.27 | 24.2505 | 24.231 | 24.2115 | 24.1921 |
| Support_Force | J”-K | East | Filchner_Ice_Shelf | 1 | 9.72 | 9.361 | 9.72 | 9.73057 | 9.74115 | 9.75172 | 9.7623 |
| Recovery | J”-K | East | Filchner_Ice_Shelf | 1 | 41.05 | 41.05 | 41.05 | 41.1242 | 41.1983 | 41.2725 | 41.3467 |
| Slessor | J”-K | East | Filchner_Ice_Shelf | 1 | 26.11 | 24.916 | 26.11 | 26.1256 | 26.1412 | 26.1568 | 26.1724 |
| Bailey | J”-K | East | Filchner_Ice_Shelf | 1 | 8.98 | 8.61 | 8.98 | 9.00126 | 9.02252 | 9.04378 | 9.06504 |
| Islands | nan | Islands | Islands | 1 | 76.9899 | 76.9899 | 76.9899 | 76.9899 | 76.9899 | 76.9899 | 76.9899 |
| Sum | All | All | All | nan | 2097.57 | 2236.96 | 2126.64 | 2133.49 | 2140.25 | 2147.01 | 2153.77 |
WARNING: Using shelf vs. non-shelf is important and can be done for all AQ, but Island discharge (~77 Gt/year) doesn’t provide enough metadata to break down by east/west/peninsula.
<<load_rignot>>
c = np.arange(2010,2017+1)
dd = df.groupby(['shelf','Region']).sum().drop(columns=['Basin','Subregion'])[c].mean(axis='columns')
dd.loc['Non-shelf discharge'] = dd.loc[0,:].sum()
dd.loc['shelf discharge'] = dd.loc[1,:].sum()
dd['Total discharge'] = dd.loc[['Non-shelf discharge','shelf discharge']].sum()
dd
# df_shelf = df[df['shelf'] == 1][c].mean(axis='columns')
# df_noshelf = df[df['shelf'] == 0][c].mean(axis='columns')
# df_shelf
# print("Total discharge: ", df[df['shelf'] >= 0][c].mean(axis='columns').sum())
# print('Shelf discharge: ', df_shelf.sum())
# print('Non-shelf discharge: ', df_noshelf.sum())
shelf Region
0.0 East 177.140000
Peninsula 131.398873
West 23.003920
1.0 East 926.544384
Islands 76.989900
Peninsula 205.140504
West 768.695078
Non-shelf discharge 331.542793
shelf discharge 1977.369866
Total discharge 2308.912659
dtype: float64
Don’t use mass estimates from Rignot 2019 because SMB is not provided with constituent terms, nor with temporal resolution aligned with this effort.
disch = df.loc['Sum'][c].mean()
disch_all = df.loc['Sum'][np.arange(1979,2017+1)].mean()
smb = df['SMB']['Sum']
smb_MAR_1980_2017 = 2267.03809702665
smb_MAR_2010_2019 = 2588.33462654902
print("Discharge (2010-2017)", disch)
print("Discharge (1979-2017)", disch_all)
print("Rignot SMB (1979-2008)", smb)
print("")
print("MB dall - smball", smb - disch_all)
print("MB D2010 - smb MAR 2010", smb_MAR_2010_2019 - disch)
print("MB D2010 - smb MAR 2010", smb_MAR_1980_2017 - disch)
Discharge (2010-2017) 2308.912659378624 Discharge (1979-2017) 2219.589344623233 Rignot SMB (1979-2008) 2097.5676000000003 MB dall - smball -122.02174462323273 MB D2010 - smb MAR 2010 279.4219671703959 MB D2010 - smb MAR 2010 -41.874562351973964
This is steady-state discharge from grounded ice to shelves.
import numpy as np
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='Discharge', index_col = 1, skiprows = 3)
df = df[df.columns[1::2]]
df.columns = [np.floor(c).astype(int) for c in df.columns]
df = df.drop(index=df.index[0])
df = df.drop(index='Antarctic Ice Shelves')
df = df[np.arange(2010,2020)].mean(axis='columns')
df.name = 'Mass'
df
Abbot 32.473268
Ainsworth 0.157966
Alison 2.985331
Amery 78.564587
Andreyev 2.207105
...
Withrow 0.480019
Wordie 7.754308
Wylde 0.005026
Zelee 0.42351
Zubchatyy 0.469816
Name: Mass, Length: 162, dtype: object
<<load_davison_discharge>>
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.drop(columns=['latitude','longitude'])
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.loc['Total'] = [df['Mass'].sum(), None, 'All']
dd = df[['Mass','Region']].groupby('Region').sum()
dd.loc['Total'] = dd.sum()
dd
| Region | Mass |
|---|---|
| East | 923.794 |
| Islands | 1.28338 |
| Peninsula | 152.536 |
| West | 857.468 |
| Total | 1935.08 |
g.mapset -c MAR
ncdump -v TIME dat/MARv3.12-ANT-35km-annual.nc4 # 30-39 = 2010-2019
ncra --overwrite -d TIME,30,39 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
r.mapcalc "MASK = if(MSK > 50)" --o
r.mapcalc "scale_mask = MSK / 100" # if only X % of a cell is ice, scale by that.
# 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) * scale_mask / exp(10,12)"
done
r.mapcalc "RFZ = ME + RF - RU"r.mask --o raster=ground@PERMANENT --q
for var in SMB; 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 ${var} | grep sum
echo "#"; echo "#"
done
r.mask -r --q�[?2004l?2004lSMB East 1245.91548135353 West 670.071679116414 Peninsula 258.356884650752 Islands 57.2462599089497 �[01;31m�[Ksum�[m�[K=2231.59030502963
r.mask --o raster=basins@PERMANENT --q
for var in RF CON DEP SF RFZ EVA RU SUB; do
echo -n "${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�[?2004lRF East 1.55632116250339 West 0.808106668364101 Peninsula 4.09478244665841 Islands 0.138051253656 �[01;31m�[Ksum�[m�[K=6.59726153118188 CON East 0.00661255569554064 West 0.00272055846697959 Peninsula 0.0419858971970306 Islands 0.0004622996319 �[01;31m�[Ksum�[m�[K=0.0517813109914508 DEP East 41.665876584542 West 27.9828632785555 Peninsula 6.63393628684254 Islands 2.5341322486399 �[01;31m�[Ksum�[m�[K=78.8168083985797 SF East 1533.3202293162 West 863.167047001661 Peninsula 322.457642541089 Islands 55.9839132874438 �[01;31m�[Ksum�[m�[K=2774.92883214639 RFZ East 41.6358385500057 West 15.8325757324401 Peninsula 46.6152538919879 Islands 2.5265341720841 �[01;31m�[Ksum�[m�[K=106.610202346517 EVA East 1.28114312886215 West 0.469596624150337 Peninsula 1.29873172532395 Islands 0.018721805005 �[01;31m�[Ksum�[m�[K=3.06819328334145 RU East 2.98889276511441 West 0.05457606699 Peninsula 4.76209158620797 Islands 0.0014503067633 �[01;31m�[Ksum�[m�[K=7.80701072507568 SUB East 171.750571828021 West 41.4080900874875 Peninsula 15.7626191697665 Islands 1.3719237929954 �[01;31m�[Ksum�[m�[K=230.293204878272 [Raster MASK present]
Van Liefferinge (2013) http://doi.org/10.5194/cp-9-2335-2013
Convert MAT file to XYZ for importing into GRASS
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()
| 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 |
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=, --oecho "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"|" -tAll: 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
20/69 % = 28.9855072464
import pandas as pd
fname = "/home/kdm/data/Greene_2022/data/greene_Supplementary_Table_1.xlsx"
df = pd.read_excel(fname, index_col=1, skiprows=4)
df = df.drop(index=df.index[0])
df = df.drop(index=['Antarctica'])
df = df[df.columns[[1,2,9]]]
df.columns = ['latitude','longitude','Mass']
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']
dd = df[['Mass','Region']].groupby('Region').sum()
dd.loc['Total'] = dd.sum(axis='rows')
dd
| Region | Mass |
|---|---|
| 44.7053 | |
| East | 694.1 |
| Islands | 1.51803 |
| Peninsula | 103.676 |
| West | 566.998 |
| Total | 1411 |
import pandas as pd
import numpy as np
fname = "/home/kdm/data/Greene_2022/data/greene_Supplementary_Table_1.xlsx"
df = pd.read_excel(fname, index_col=1, skiprows=4)
##############################################################################
###
### cleanup
###
df = df.drop(index=df.index[0])
df = df.drop(index=['Antarctica'])
lon = df['Unnamed: 3']
lat = df['Unnamed: 2']
for c in df.columns: # Drop extra columns
if 'Unnamed' in str(c):
df = df.drop(columns=c)
if 'Gt/yr' in str(c):
df = df.drop(columns=c)
if ('control run' in str(c)) | ('instantaneous' in str(c)):
df = df.drop(columns=c)
for c in df.columns:
if type(c) == str: df = df.drop(columns=c)
# df = df.drop(columns=[2000.75])
# df = df.drop(columns=[1997.75])
# df.columns = df.columns.round().astype(int)
##############################################################################
<<load_greene_2022_adv_ret>>
c = df.columns
diff = df.diff(axis='columns')[c]
diff_gain = diff[diff > 0].sum(axis='columns')
diff_loss = diff[diff < 0].sum(axis='columns')
diff_gain.name = 'Mass'
diff_loss.name = 'Mass'
df_gain = pd.DataFrame(diff_gain)
df_loss = pd.DataFrame(diff_loss)
df_net = df_loss + df_gain
print("Net:")
print('Mass gain', df_net[df_net > 0].sum(axis='rows').values)
print('Mass Loss', df_net[df_net < 0].sum(axis='rows').values)
print('Net mass change', df_net.sum(axis='rows').values)
dt = df.columns[-1] - df.columns[0]
print("\nPer year:")
print('Mass gain', df_net[df_net > 0].sum(axis='rows').values / dt)
print('Mass Loss', df_net[df_net < 0].sum(axis='rows').values / dt)
print('Net mass change', df_net.sum(axis='rows').values / dt)
Net: Mass gain [4563.264309048649] Mass Loss [-9434.897965610035] Net mass change [-4871.633656561384] Per year: Mass gain [194.59549292318295] Mass Loss [-402.3410646315572] Net mass change [-207.74557170837417]
- Most numbers here match what’s in the publication
- Neither the total nor Ronne match.
- Here, total is 4871 Gt net change.
- Below, Ronne net loss is 1031
- From the paper (paragraph under Fig. 2)
- Total should be 5874 (missing 5874-4871 = 1003)
- Ronne should be 2034 (missing 2034-1031 = 1003)
- But Thwaites, Larsen C, and Ross West match paper, so it seems like I’m parsing the dataset correctly.
- Filchner matches mass gain.
Find the top 10 shelves with net and gross mass gain and loss total (summed) over the period
tmp = pd.DataFrame(index=np.arange(10))
tmp['Net gain: Name'] = df_net.sort_values(by='Mass', ascending=False).head(10).index
tmp['Net gain: Mass'] = df_net.sort_values(by='Mass', ascending=False).head(10)['Mass'].values
tmp['Net loss: Name'] = df_net.sort_values(by='Mass', ascending=True).head(10).index
tmp['Net loss: Mass'] = df_net.sort_values(by='Mass', ascending=True).head(10)['Mass'].values
tmp['Gross gain: Name'] = df_gain.sort_values(by='Mass', ascending=False).head(10).index
tmp['Gross gain: Mass'] = df_gain.sort_values(by='Mass', ascending=False).head(10)['Mass'].values
tmp['Gross loss: Name'] = df_loss.sort_values(by='Mass', ascending=True).head(10).index
tmp['Gross loss: Mass'] = df_loss.sort_values(by='Mass', ascending=True).head(10)['Mass'].values
tmp
| Net gain: Name | Net gain: Mass | Net loss: Name | Net loss: Mass | Gross gain: Name | Gross gain: Mass | Gross loss: Name | Gross loss: Mass | |
|---|---|---|---|---|---|---|---|---|
| 0 | Filchner | 1796.26 | Thwaites | -1968.41 | Ronne | 2762.7 | Ronne | -3794.31 |
| 1 | Amery | 569.883 | Larsen C | -1166.92 | Ross West | 1968.02 | Ross West | -2897.63 |
| 2 | Cook | 414.571 | Ronne | -1031.61 | Filchner | 1843.53 | Thwaites | -2248.52 |
| 3 | Shackleton | 369.773 | Ross West | -929.617 | Amery | 917.121 | Larsen C | -1619.1 |
| 4 | Brunt Stancomb | 362.787 | Wilkins | -622.156 | Ross East | 857.219 | Pine Island | -1231.67 |
| 5 | West | 278.333 | Larsen B | -530.038 | Pine Island | 780.117 | Ross East | -1135.23 |
| 6 | Jelbart | 169.419 | Pine Island | -451.554 | Brunt Stancomb | 493.663 | Ninnis | -675.229 |
| 7 | Fimbul | 148.221 | Mertz | -381.971 | Shackleton | 493.661 | Wilkins | -642.061 |
| 8 | Riiser-Larsen | 84.376 | Ninnis | -300.936 | Cook | 454.636 | Larsen B | -584.603 |
| 9 | Pourquoi Pas | 64.9883 | Larsen A | -286.377 | Larsen C | 452.177 | Mertz | -571.076 |
Now convert to Gt/year
for col in tmp.columns:
if 'Mass' in col: tmp[col] = tmp[col] / c.size
tmp
| Net gain: Name | Net gain: Mass | Net loss: Name | Net loss: Mass | Gross gain: Name | Gross gain: Mass | Gross loss: Name | Gross loss: Mass | |
|---|---|---|---|---|---|---|---|---|
| 0 | Filchner | 74.8441 | Thwaites | -82.0169 | Ronne | 115.113 | Ronne | -158.096 |
| 1 | Amery | 23.7451 | Larsen C | -48.6219 | Ross West | 82.0007 | Ross West | -120.735 |
| 2 | Cook | 17.2738 | Ronne | -42.9837 | Filchner | 76.8138 | Thwaites | -93.6885 |
| 3 | Shackleton | 15.4072 | Ross West | -38.734 | Amery | 38.2134 | Larsen C | -67.4626 |
| 4 | Brunt Stancomb | 15.1161 | Wilkins | -25.9232 | Ross East | 35.7174 | Pine Island | -51.3196 |
| 5 | West | 11.5972 | Larsen B | -22.0849 | Pine Island | 32.5049 | Ross East | -47.3011 |
| 6 | Jelbart | 7.05912 | Pine Island | -18.8147 | Brunt Stancomb | 20.5693 | Ninnis | -28.1346 |
| 7 | Fimbul | 6.17586 | Mertz | -15.9155 | Shackleton | 20.5692 | Wilkins | -26.7525 |
| 8 | Riiser-Larsen | 3.51567 | Ninnis | -12.539 | Cook | 18.9432 | Larsen B | -24.3585 |
| 9 | Pourquoi Pas | 2.70785 | Larsen A | -11.9324 | Larsen C | 18.8407 | Mertz | -23.7948 |
<<green_2022_mean>> # provides df_net
# df = df_net
df['longitude'] = lon
df['latitude'] = lat
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','geometry'])
# df.loc['Total'] = [df['Mass'].sum(), None, 'All']
# df.groupby('Region').sum().round()
diff = df[c].diff(axis='columns')
diff_gain = diff[diff > 0].sum(axis='columns')
diff_loss = diff[diff < 0].sum(axis='columns')
diff_gain.name = 'Mass'
diff_loss.name = 'Mass'
df_gain = pd.DataFrame(diff_gain)
df_loss = pd.DataFrame(diff_loss)
df_net = df_loss + df_gain
df_gain['Region'] = df['Region']
df_loss['Region'] = df['Region']
df_net['Region'] = df['Region']
Net: Mass gain [4563.264309048649] Mass Loss [-9434.897965610035] Net mass change [-4871.633656561384] Per year: Mass gain [194.59549292318295] Mass Loss [-402.3410646315572] Net mass change [-207.74557170837417]
for loc in ['East','West','Peninsula']:
print("\n", loc)
sub = (df_net['Mass'] > 0) & (df_net['Region'] == loc); print('Mass gain', df_net[sub].drop(columns='Region').sum().values/dt)
sub = (df_net['Mass'] < 0) & (df_net['Region'] == loc); print('Mass loss', df_net[sub].drop(columns='Region').sum().values/dt)
East Mass gain [192.48919217062863] Mass loss [-69.4663382466161] West Mass gain [1.923849262408337] Mass loss [-206.1686424710849] Peninsula Mass gain [0.17175689689132026] Mass loss [-124.70409821345615]
- Davison (2023) http://doi.org/10.1126/sciadv.adi0186
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()
/tmp/ipykernel_2536812/879752930.py:31: 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 |
Same as above, different sheet. Reuses variables from above, run that first.
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()
/tmp/ipykernel_2536812/353247760.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 |
[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.
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
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',2010.2,2021.2]]
df = df.iloc[1:]
# Remove last two rows
aq = df.loc['Antarctica']
other = df.loc['Other']
df = df.iloc[:-2]
print(df.sum())
print("")
print(aq)
print("")
print(other)
lat -12882.373098 lon 6279.268331 2010.2 679991.871066 2021.2 681213.775349 dtype: object lat -90 lon every 2010.2 24595086 2021.2 24596304 Name: Antarctica, dtype: object lat NaN lon NaN 2010.2 23915094 2021.2 23915090 Name: Other, dtype: object
shelf = df.sum()
print("All AQ loss: ", (aq[2021.2] - aq[2010.2]) / (2021-2010))
print("Named shelf loss: ", (shelf[2021.2] - shelf[2010.2]) / (2021-2010))
print("Other loss: ", (other[2021.2] - other[2010.2]) / (2021-2010))
print("Named + Other: ", (((other + shelf)[2021.2] - (other + shelf)[2010.2]) / (2021-2010)))
print("Named %: ", 2.19/63.02*100)
All AQ loss: 110.72727272727273 Named shelf loss: 111.08220751989971 Other loss: -0.36363636363636365 Named + Other: 110.71857115626335 Named %: 3.4750872738813077
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()
| 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 |
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.drop(columns='geometry').sum(axis='rows')
gdf.loc['Total', 'Region'] = 'All'
gdf['frontal change'] = (gdf[2021.2] - gdf[2010.2]) / (2021.2-2010.2)
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())
neg frontal change Region East 51.964246 Islands 0.134919 Peninsula 115.849899 West 222.842824 pos frontal change Region All 111.082208 East 249.493215 Islands 0.003094 Peninsula 5.055953 West 247.321834 all frontal change Region All 111.082208 East 197.528968 Islands 0.131825 Peninsula 110.793946 West 24.47901 /tmp/ipykernel_2536812/507091481.py:15: 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. gdf.loc['Total'] = gdf.drop(columns='geometry').sum(axis='rows')
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
import xarray as xr
ds = xr.open_mfdataset("~/data/Paolo_2023/ANT_G1920V01_IceShelfMelt.nc")
ds = ds[['melt','melt_err']].sel({'time':slice('2010-01-01','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)
[########################################] | 100% Completed | 5.55 s
<xarray.Dataset> 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<chunksize=(486, 486), meta=np.ndarray>
melt_err (y, x) float32 34MB dask.array<chunksize=(486, 486), meta=np.ndarray>
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
EOFecho "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�[?2004l NET �[?2004l East -327.749700568986 West -476.184170767834 Peninsula -140.389218858182 �[?2004l East 1261.7643241234 West 1501.03281071649 Peninsula 225.909323225931 �[?2004l �[01;31m�[Ksum�[m�[K=-977.26950720935 �[?2004l �[?2004l FREEZE_ON �[?2004l East 204.212024518552 West 174.058868541697 Peninsula 16.7004224367821 �[?2004l East 449.068552754539 West 579.006947989805 Peninsula 50.2702348521852 �[?2004l �[01;31m�[Ksum�[m�[K=404.684807702491 �[?2004l �[?2004l MELT_OFF �[?2004l East -531.961725087539 West -650.243039309524 Peninsula -157.089641294966 �[?2004l East 812.695771368885 West 922.025862726728 Peninsula 175.639088373746 �[?2004l �[01;31m�[Ksum�[m�[K=-1381.95431491188
import xarray as xr
ds = xr.open_dataset("~/data/Groh_2021/AIS_GMB_grid.nc")
ds['dm'] = ds['dm'] * ds['area']
ds = ds['dm'].sum(dim=['x','y'])/1E12
df = ds.to_dataframe()
# df = df.loc['2002-01-01':'2024-12-31']
df = df - df.max()
_ = df.plot()
# df = df.resample('D').mean().interpolate().resample('YS').mean()
df = df.resample('YS').mean()
df = df.diff()
print(df.mean())
dm -79.146737 dtype: float64
(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")))
)