On Ice Sheet Mass Flow

Introduction

Sankey diagrams for mass flow in Greenland and Antarctica.

Abstract & Methods

See ./ms.tex

Results

Note

All figures are width-proportional within and between each other.

Implementation

Greenland

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)

Reset

trash G_GL
[[ -e ./G_GL ]] || grass -e -c EPSG:3413 ./G_GL

Discharge

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

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

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

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.

SMB

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"

for var in SF RF RU ME SMB EVA CON DEP SUB RFZ; do
  echo ${var} $(r.univar -g ${var} | grep sum)
done

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

Discharge

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]

Antarctica

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)

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)

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)

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)

Export tables to CSV

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

Reset

trash G_AQ
[[ -e ./G_AQ ]] || grass -e -c EPSG:3031 ./G_AQ

Masks: East, West, Peninsula, Islands, Grounded and Shelves

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

SMB (MAR)

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"

Stats

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

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]

Basal melt

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=, --o

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

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

20/69 % = 28.9855072464

Antarctic Ice shelves

Submarine melt

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_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

Calving

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_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

Discharge

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=((1,2)))

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()

/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

Frontal Retreat

[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',2000.2,2021.2]]
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
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

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)

All AQ loss:  -63.142857142857146
Named shelf loss:  -60.83361628651619
Other loss:  -2.1904761904761907
Named + Other:  -63.02409247699238
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.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())

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

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

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)

[########################################] | 100% Completed | 7.79 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
EOF

Stats

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

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

Misc

Export tables to CSVs

(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")))
  )

Name		Name	Last commit message	Last commit date
Latest commit History 105 Commits
dat		dat
tex		tex
Makefile		Makefile
README.org		README.org
credit.csv		credit.csv
fig_aq_gl.png		fig_aq_gl.png
fig_aq_parts.png		fig_aq_parts.png
legend.svg		legend.svg
library.bib		library.bib
ms.org		ms.org
sankey.tex		sankey.tex

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On Ice Sheet Mass Flow

Table of contents

Introduction

Abstract & Methods

Results

Implementation

Greenland

Reset

Discharge

Basal melt

GZ retreat

SMB

Discharge

Antarctica

Export tables to CSV

Reset

Masks: East, West, Peninsula, Islands, Grounded and Shelves

SMB (MAR)

Stats

Basal melt

Antarctic Ice shelves

Submarine melt

Calving

Discharge

Frontal Retreat

GZ retreat

Shelf freeze/melt

Stats

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Export tables to CSVs

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Repository files navigation

On Ice Sheet Mass Flow

Table of contents

Introduction

Abstract & Methods

Results

Implementation

Greenland

Reset

Discharge

Basal melt

GZ retreat

SMB

Discharge

Antarctica

Export tables to CSV

Reset

Masks: East, West, Peninsula, Islands, Grounded and Shelves

SMB (MAR)

Stats

Basal melt

Antarctic Ice shelves

Submarine melt

Calving

Discharge

Frontal Retreat

GZ retreat

Shelf freeze/melt

Stats

Misc

Export tables to CSVs

Convert PDFs to PNG

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