Sankey diagrams for mass flow in Greenland and Antarctica.
See ./ms.tex
Note
All figures are width-proportional within and between each other.
| Code | Term | Value | I/O | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 45 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 5 | I | 2000-2019 | fettweis_2020 | guesstimate |
| DP | Deposition | 10 | I | 2000-2019 | fettweis_2020 | guesstimate |
| SF | Snowfall | 685 | I | 2000-2019 | fettweis_2019 | |
| RFZ | Refreezing | 195 | IO | 2000-2019 | fettweis_2020 | RFZ = ME + RF - RU |
| EV | Evaporation | 10 | O | 2000-2019 | fettweis_2020 | guesstimate |
| RU | Runoff | 440 | O | 2000-2019 | fettweis_2020 | |
| BM | Basal melting | 20 | O | steady | karlsson_2020 | |
| DYN | Discharge | 490 | - | 2000-2019 | mankoff_2020 | Submarine melting + calving |
| SUB | Submarine melting | 245 | O | enderlin_2013 | 50 % of discharge | |
| ICE | Calving | 245 | O | enderlin_2013 | 50 % of discharge | |
| GZRET | Grounding line retreat ? | 5 | O | See methods | guesstimate | |
| FRLOSS | Frontal retreat | 50 | O | 2000-2020 | kochtitzky_2023 | |
| FRGAIN | Frontal advance | 0 | O | None in GL | ||
| SU | Sublimation | 60 | O | 2000-2019 | fettweis_2020 | |
| DD | Drawdown | - | Derived | sum(O) - sum(I) | ||
| MG | Mass gain | - | Derived | sum(I) - sum(O) |
trash G_GL
[[ -e ./G_GL ]] || grass -e -c EPSG:3413 ./G_GL- 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 # 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)
doneSF 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
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]
487
| Code | Term | Value | I/O | Period | Source | Comment |
|---|---|---|---|---|---|---|
| SMB | Surface mass balance | 2021 | IO | 1979-2008 | rignot_2019 table 1 | 2098AQ-77Islands=2021 |
| SMB | Surface mass balance | 2582 | IO | 2000-2019 | fettweis_2020 | WITH shelves |
| DYN | Discharge | 2229 | O | 2009-2017 | rignot_2019 table 1 | 2306-77 |
Davison 2023
| DIS | Discharge | 1838 | O | ?? | davison_2023 | SUM(C7:C168) |
| BM | Basal melt | 902 | O | SUM(E7:E168) | ||
| C | Calving | 1348 | O | SUM(G7:G168) | ||
| SMB | Surface mass balance | 412 | I | SUM(I7:I168) shelf only |
902+1348-412 = 1838
| Code | Term | Value | I/O | Period | Source | Comment |
|---|---|---|---|---|---|---|
| RF | Rainfall | 5 | I | 2000-2019 | fettweis_2020 | |
| CD | Condensation | 1 | I | 2000-2019 | fettweis_2020 | |
| DP | Deposition | 80 | 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 | 1090 + 1545 | - | |||
| SUB | Submarine melting | 1090 | O | davison_2023 | ||
| ICE | Calving | 1545 | O | davison_2023 | ||
| GZRET | Grounding line retreat ? | 1 | O | 45 in Amundsen from Davison email | ||
| FRLOSS | Frontal retreat | 79+122+145-1 | O | greene_2022 | ||
| FRGAIN | Frontal advance | 181+1+103 | O | 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) |
Exported aq_baseline to ./dat/aq_baseline.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_GR use=val val=1 where='((Regions == "East") AND (TYPE == "GR"))'
v.to.rast input=basins output=east_FL use=val val=11 where='((Regions == "East") AND (TYPE == "FL"))'
v.to.rast input=basins output=west_GR use=val val=2 where='((Regions == "West") AND (TYPE == "GR"))'
v.to.rast input=basins output=west_FL use=val val=12 where='((Regions == "West") AND (TYPE == "FL"))'
v.to.rast input=basins output=peninsula_GR use=val val=3 where='((Regions == "Peninsula") AND (TYPE == "GR"))'
v.to.rast input=basins output=peninsula_FL use=val val=13 where='((Regions == "Peninsula") AND (TYPE == "FL"))'
v.to.rast input=basins output=islands use=val val=4 where='(TYPE == "IS")'
r.patch input=east_GR,east_FL,west_GR,west_FL,peninsula_GR,peninsula_FL,islands output=basins
r.category basins separator=":" rules=- << EOF
1:East (Grounded)
11:East (Floating)
2:West (Grounded)
12:West (Floating)
3:Peninsula (Grounded)
13:Peninsula (Floating)
4:Islands
EOF
r.colors map=basins color=viridisg.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} = (${var}/1000) * 1000 * (AREA * 1000*1000) / exp(10,12)"
done
r.mapcalc "RFZ = ME + RF - RU"r.mask --o raster=basins@PERMANENT --q maskcats="1 thru 3 10 thru 20" # drop 0 and Islands
for var in SF RF RU ME SMB EVA CON DEP SUB RFZ; do
echo -n "${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[Raster MASK present] SF East (Grounded) 1383.5170055779 West (Grounded) 688.480301166503 Peninsula (Grounded) 270.324169435779 East (Floating) 172.41137746281 West (Floating) 180.27593549343 Peninsula (Floating) 56.6841289993761 sum=2751.6929181358 RF East (Grounded) 0.53173174128475 West (Grounded) 0.22197445329555 Peninsula (Grounded) 2.1110471117335 East (Floating) 0.842541426357001 West (Floating) 0.4498711186449 Peninsula (Floating) 2.307238481508 sum=6.46440433282369 RU East (Grounded) 1.53022074184155 West (Grounded) 0.0059355454226 Peninsula (Grounded) 1.8834556710495 East (Floating) 1.5089940427256 West (Floating) 0.0304982132294 Peninsula (Floating) 4.35827769837335 sum=9.317381912642 ME East (Grounded) 15.4802688824357 West (Grounded) 4.3355515366436 Peninsula (Grounded) 17.3666975625616 East (Floating) 26.407263870097 West (Floating) 9.2284017518 Peninsula (Floating) 34.440989714197 sum=107.259173317735 SMB East (Grounded) 1268.09953548228 West (Grounded) 678.756441138014 Peninsula (Grounded) 262.863634061747 East (Floating) 153.249402230902 West (Floating) 177.921656614902 Peninsula (Floating) 51.4267222532681 sum=2592.31739178111 EVA East (Grounded) 0.60507218584105 West (Grounded) 0.195446970963 Peninsula (Grounded) 0.68838986544795 East (Floating) 0.70256713317005 West (Floating) 0.2422464141299 Peninsula (Floating) 0.70164322473235 sum=3.1353657942843 CON East (Grounded) 0.00144321189675 West (Grounded) 0.00237202472355 Peninsula (Grounded) 0.01264862146645 East (Floating) 0.0031724865901 West (Floating) 0.0019547481581 Peninsula (Floating) 0.03522553443475 sum=0.0568166272697001 DEP East (Grounded) 36.3995731586915 West (Grounded) 22.3853823370692 Peninsula (Grounded) 5.3339705229687 East (Floating) 5.70103389655939 West (Floating) 6.05853236904585 Peninsula (Floating) 1.5062480433876 sum=77.384740327722 SUB East (Grounded) 150.624482553971 West (Grounded) 32.2385823068849 Peninsula (Grounded) 12.2348077688132 East (Floating) 23.4661462650309 West (Floating) 8.5418917438099 Peninsula (Floating) 3.94097993607855 sum=231.046890574587 RFZ East (Grounded) 14.4817798818789 West (Grounded) 4.55159044451654 Peninsula (Grounded) 17.5942890032456 East (Floating) 25.7408112537284 West (Floating) 9.64777465721551 Peninsula (Floating) 32.3899504973317 sum=104.406195737917 [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
melt = pd.DataFrame(np.array([X,Y,m]).T, columns=['x','y','melt'])\
.dropna()
melt.to_csv('./tmp/melt.csv', header=False, index=False)
melt.head()
| x | y | melt | |
|---|---|---|---|
| 148741 | 1.045e+06 | -2.14e+06 | 1e-09 |
| 149859 | 1.03e+06 | -2.135e+06 | 0.00146608 |
| 149860 | 1.035e+06 | -2.135e+06 | 0.000266042 |
| 149861 | 1.04e+06 | -2.135e+06 | 1e-09 |
| 149862 | 1.045e+06 | -2.135e+06 | 0.00045698 |
grass ./G_AQ/PERMANENT
g.mapset -c liefferinge_2023
r.in.xyz input=./tmp/melt.csv output=melt sep=, --oecho "All: " $(r.univar -g map=melt | grep sum)
echo ""
r.univar -gt map=melt zones=basins | cut -d"|" -f2,13 | column -s"|" -tAll: sum=69.3982306335468 label sum East (Grounded) 45.7178033424208 West (Grounded) 18.0714170862276 Peninsula (Grounded) 2.93302497694997 Islands 0.279139711405429 East (Floating) 1.03624592705523 West (Floating) 0.781445329564939 Peninsula (Floating) 0.254017664974735
- 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='Melt',
index_col = 1, skiprows = 3)
# 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[df.columns[3:]]
df = df.iloc[1:]
df = pd.DataFrame(df.mean(axis='columns'))
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_1220053/4002790239.py:43: 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 | 1087.2 |
| East | 321.123 |
| Peninsula | 172.74 |
| West | 593.128 |
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='Calving',
index_col = 1, skiprows = 3)
# 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[df.columns[3:]]
df = df.iloc[1:]
df = pd.DataFrame(df.mean(axis='columns'))
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_1220053/519284814.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 | 1543.55 |
| East | 560.3 |
| Peninsula | 236.416 |
| West | 745.595 |
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
[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
(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")))
)