GL_discharge.org

Table of contents

• Introduction
  • WARNING
  • Data example
    • Printout
    • Plot: Annual discharge per region
    • Table: Annual discharge per region
• Fetch data
• Reprocess

Introduction

Greenland discharge is repackaged from:

• Paper: Mankoff (2020) http://doi.org/10.5194/essd-12-1367-2020
• Code (original): https://github.com/GEUS-Glaciology-and-Climate/ice_discharge
• Data: https://doi.org/10.22008/promice/data/ice_discharge/d/v02

WARNING

This product is generated from flux gates ~5 km upstream of the terminus.

It lacks

• Terminus retreat, which should add ~10 % over the last two decades
  • Greene (2024) http://doi.org/10.1038/s41586-023-06863-2
  • Kochtitzky (2023) http://doi.org/10.1029/2023gl104095
• Splitting output between icebergs and submarine melt, which is ~50 % +- 30 %
  • Rignot (2010) http://doi.org/10.1038/NGEO765
• Peripheral glaciers
  • Adds 5 Gt/yr for recent years from Bollen (2023) http://doi.org/10.1017/jog.2022.52

Data example

Printout

<xarray.Dataset> Size: 12kB
Dimensions:      (region: 7, time: 186)
Coordinates:
  * time         (time) datetime64[ns] 1kB 1840-01-01 1841-01-01 ... 2025-01-01
  * region       (region) int32 28B 1 2 3 4 5 6 7
Data variables:
    discharge    (region, time) float32 5kB ...
    err          (region, time) float32 5kB ...
    region_name  (region) <U2 56B ...
Attributes: (12/14)
    featureType:      timeSeries
    title:            Greenland ice sheet discharge by Mouginot region
    summary:          Greenland ice sheet discharge by Mouginot region
    keywords:         Greenland; Mass; Mass balance
    source:           git commit: 54a4452
    creator_name:     Ken Mankoff
    ...               ...
    institution:      NASA GISS
    references:       TBD
    product_version:  1.0
    history:          TBD
    Conventions:      CF-1.8
    DOI:              https://doi.org/10.5281/zenodo.14020895

Plot: Annual discharge per region

import xarray as xr
ds = xr.open_dataset('dat/GL_discharge.nc')
df = ds['discharge'].to_dataframe()['discharge']

df = df.unstack().T
df.columns = ds['region_name'].values
ax = df.plot(drawstyle='steps-post')
_ = ax.set_ylabel('Discharge [Gt yr$^{-1}$]')

Table: Annual discharge per region

import xarray as xr
ds = xr.open_dataset('dat/GL_discharge.nc')
ds = ds.sel({'time': slice('1800-01-01','2024-12-31')})
df = ds['discharge'].to_dataframe()['discharge']
df = df.unstack().T
df.columns = [str(_[0]) + ' ['+_[1]+']' for _ in zip(ds['region'].values, ds['region_name'].values)]
df['GL'] = df.sum(axis='columns')
df.index = [str(_)[0:4] for _ in df.index]
df.round()

	1 [CE]	2 [CW]	3 [NE]	4 [NO]	5 [NW]	6 [SE]	7 [SW]	GL
1840	61	61	20	21	83	118	18	382
1841	61	61	20	21	83	118	18	382
1842	61	61	20	21	83	118	18	382
1843	61	61	20	21	83	118	18	382
1844	61	61	20	21	83	118	18	382
1845	62	62	20	22	85	121	18	391
1846	62	62	20	22	85	120	18	388
1847	62	62	20	22	85	121	18	389
1848	63	63	21	22	87	124	18	399
1849	63	63	21	22	87	123	18	397
1850	63	63	21	22	87	124	18	399
1851	62	62	20	22	85	121	18	391
1852	64	64	21	22	88	125	19	403
1853	65	66	22	23	90	128	19	412
1854	65	65	21	23	89	127	19	409
1855	65	65	21	23	89	126	19	407
1856	64	64	21	23	89	126	19	406
1857	66	66	22	23	90	128	19	414
1858	65	65	21	23	90	127	19	411
1859	64	64	21	22	88	125	19	404
1860	64	64	21	22	88	125	19	402
1861	63	64	21	22	87	124	18	400
1862	63	63	21	22	86	122	18	395
1863	60	60	20	21	82	117	17	378
1864	58	59	19	20	80	114	17	368
1865	58	58	19	20	80	114	17	367
1866	57	57	19	20	79	112	17	361
1867	59	59	19	21	81	115	17	369
1868	58	58	19	20	80	113	17	366
1869	59	59	19	21	81	116	17	373
1870	60	60	20	21	83	117	17	378
1871	62	62	20	22	85	120	18	388
1872	63	64	21	22	87	124	18	400
1873	63	63	21	22	86	122	18	395
1874	64	64	21	22	88	125	19	402
1875	64	64	21	22	88	125	19	402
1876	64	64	21	22	87	124	18	401
1877	64	64	21	22	88	125	19	402
1878	63	63	21	22	86	122	18	395
1879	63	63	21	22	87	123	18	398
1880	63	64	21	22	87	124	18	400
1881	64	64	21	22	88	124	19	401
1882	64	64	21	22	87	124	18	401
1883	63	63	21	22	86	123	18	396
1884	62	62	20	22	85	121	18	391
1885	62	62	20	22	85	121	18	391
1886	62	62	20	22	85	120	18	388
1887	62	62	20	22	85	120	18	388
1888	63	64	21	22	87	124	18	400
1889	63	63	21	22	87	123	18	397
1890	64	64	21	22	87	124	18	401
1891	64	64	21	22	88	125	19	404
1892	65	65	21	23	89	127	19	409
1893	66	66	22	23	90	128	19	414
1894	64	64	21	22	88	125	19	404
1895	65	65	21	23	89	126	19	407
1896	65	65	21	23	89	127	19	408
1897	65	65	21	23	89	126	19	407
1898	64	64	21	22	88	124	19	401
1899	63	63	21	22	87	124	18	399
1900	65	65	21	23	90	127	19	411
1901	65	65	21	23	89	126	19	407
1902	66	66	22	23	90	128	19	413
1903	65	66	22	23	90	128	19	413
1904	67	67	22	23	92	131	19	422
1905	67	67	22	23	92	130	19	421
1906	66	66	22	23	91	129	19	416
1907	67	67	22	23	92	131	19	421
1908	67	67	22	23	92	131	19	421
1909	68	68	22	24	93	132	20	425
1910	67	67	22	23	92	131	19	422
1911	67	67	22	24	92	131	20	423
1912	67	67	22	24	93	132	20	425
1913	67	67	22	24	93	131	20	424
1914	65	65	21	23	90	127	19	410
1915	65	65	21	23	89	127	19	410
1916	65	65	21	23	89	127	19	409
1917	65	65	22	23	90	127	19	411
1918	64	64	21	22	88	125	19	403
1919	63	63	21	22	87	124	18	399
1920	64	64	21	22	88	125	19	404
1921	64	64	21	22	88	124	19	401
1922	63	63	21	22	86	122	18	395
1923	63	63	21	22	86	123	18	396
1924	64	64	21	22	88	125	19	404
1925	64	64	21	23	88	125	19	405
1926	66	66	22	23	91	129	19	415
1927	67	67	22	24	92	131	20	422
1928	70	70	23	25	96	137	20	442
1929	71	71	23	25	97	138	21	445
1930	71	71	23	25	97	138	21	446
1931	74	74	24	26	102	145	22	467
1932	74	74	24	26	101	144	21	465
1933	74	74	24	26	102	144	22	466
1934	73	73	24	25	100	142	21	458
1935	73	73	24	26	101	143	21	461
1936	74	74	24	26	102	144	21	465
1937	72	72	24	25	99	140	21	453
1938	71	71	23	25	98	139	21	448
1939	71	72	24	25	98	139	21	450
1940	71	71	23	25	97	138	21	446
1941	70	71	23	25	97	138	20	444
1942	70	70	23	24	96	136	20	438
1943	69	69	23	24	95	135	20	437
1944	69	70	23	24	95	136	20	437
1945	68	68	22	24	94	133	20	429
1946	68	68	22	24	94	133	20	429
1947	67	68	22	24	93	132	20	425
1948	68	68	22	24	94	133	20	430
1949	69	69	23	24	95	135	20	435
1950	70	70	23	24	96	136	20	440
1951	71	71	23	25	97	138	21	445
1952	71	71	23	25	98	139	21	449
1953	71	71	23	25	98	139	21	448
1954	70	70	23	25	96	137	20	441
1955	69	69	23	24	94	134	20	432
1956	68	68	22	24	93	132	20	426
1957	68	68	22	24	94	133	20	430
1958	68	69	23	24	94	134	20	431
1959	68	68	22	24	94	133	20	430
1960	70	70	23	25	96	137	20	441
1961	71	71	23	25	98	139	21	447
1962	72	72	24	25	99	140	21	453
1963	70	70	23	25	97	137	20	442
1964	69	69	23	24	95	134	20	434
1965	69	69	23	24	95	135	20	436
1966	68	68	22	24	93	133	20	428
1967	67	67	22	24	93	131	20	424
1968	67	67	22	24	92	131	20	423
1969	67	67	22	24	93	132	20	424
1970	67	67	22	23	92	131	19	421
1971	67	67	22	23	92	131	19	421
1972	65	65	21	23	90	127	19	411
1973	65	65	21	23	89	126	19	408
1974	64	65	21	23	89	126	19	406
1975	65	65	21	23	89	127	19	408
1976	65	66	22	23	90	128	19	413
1977	66	66	22	23	90	128	19	414
1978	67	67	22	24	92	131	20	423
1979	68	68	22	24	93	132	20	425
1980	67	68	22	24	93	132	20	425
1981	68	68	22	24	93	132	20	426
1982	68	68	22	24	93	132	20	427
1983	66	66	22	23	91	129	19	418
1984	66	66	22	23	91	129	19	416
1985	67	67	22	23	92	131	19	421
1986	64	73	22	23	93	142	21	438
1987	65	72	23	24	90	143	21	437
1988	67	70	22	23	93	132	21	427
1989	68	71	22	23	99	126	21	431
1990	70	68	22	23	102	127	22	433
1991	70	67	23	23	97	129	22	432
1992	73	68	23	23	95	132	21	436
1993	73	67	24	23	96	132	20	435
1994	67	67	24	25	94	136	18	432
1995	62	64	22	26	91	137	18	420
1996	65	66	21	25	90	136	20	424
1997	66	68	20	23	90	134	20	421
1998	67	72	22	22	90	130	18	421
1999	66	73	22	25	89	132	18	426
2000	66	78	22	24	90	130	20	430
2001	66	79	23	23	88	125	19	424
2002	69	80	25	23	89	132	19	438
2003	73	83	24	23	92	138	20	452
2004	77	83	23	24	95	145	20	466
2005	83	83	24	23	96	146	20	475
2006	82	85	24	24	94	139	20	469
2007	78	85	24	25	94	136	19	462
2008	76	86	25	26	97	140	19	468
2009	75	88	24	23	99	143	19	472
2010	74	88	25	27	100	144	18	476
2011	75	88	25	25	104	144	20	481
2012	75	93	25	24	103	140	20	480
2013	75	94	26	24	106	142	20	486
2014	72	93	27	26	108	140	19	485
2015	72	92	28	26	108	142	19	487
2016	71	89	28	26	110	137	19	481
2017	76	81	29	27	112	147	19	490
2018	79	78	30	28	113	145	19	491
2019	80	79	30	28	109	152	19	497
2020	84	84	30	27	108	153	19	506
2021	81	89	30	27	110	151	20	507
2022	83	83	30	26	112	149	19	503
2023	80	83	30	26	110	142	18	490
2024	79	84	30	26	113	140	19	491

df.describe().round()

	1 [CE]	2 [CW]	3 [NE]	4 [NO]	5 [NW]	6 [SE]	7 [SW]	GL
count	185	185	185	185	185	185	185	185
mean	67	69	22	23	92	131	19	424
std	5	8	2	2	7	8	1	30
min	57	57	19	20	79	112	17	361
25%	64	64	21	22	88	125	18	402
50%	66	67	22	23	91	130	19	421
75%	70	71	23	24	95	136	20	438
max	84	94	30	28	113	153	22	507

Fetch data

export SERVER_URL=https://dataverse.geus.dk
export PERSISTENT_IDENTIFIER=doi:10.22008/FK2/OHI23Z
export METADATA_FORMAT=dataverse_json # ddi dataverse_json schema.org Datacite oai_datacite
curl "$SERVER_URL/api/datasets/export?exporter=$METADATA_FORMAT&persistentId=$PERSISTENT_IDENTIFIER" | jq .datasetVersion.versionNumber

890

mkdir -p tmp/greenland_discharge
pushd tmp/greenland_discharge

# wget -r -e robots=off -nH --cut-dirs=3 --content-disposition "https://dataverse.geus.dk/api/datasets/:persistentId/dirindex?persistentId=doi:10.22008/FK2/OHI23Z"
wget https://thredds.geus.dk/thredds/fileServer/MassBalance/MB_region.nc -O MB_region.nc
popd

Reprocess

• Annual discharge
• Group by ROI

import xarray as xr
import numpy as np

ds = xr.open_dataset('./tmp/greenland_discharge/MB_region.nc')

# Limit to discharge
ds = ds[['D_ROI','D_ROI_err','D']]

# Drop partial years
this_yr = ds['time'].to_series().iloc[-1].year
ds = ds.sel({'time':slice('1800',str(this_yr))})

# Scale early values to annual
ds.loc[{'time': slice('1840-01-01','1985-12-31')}] *= 365

# Resample by year
ds = ds.resample({'time':'YS'}).sum()

# Prior to 1986 there is no regional resolution, just one value for all of Greenland.

# Split into regions by taking the 1990s percent of discharge per region, and assuming the historical GIS-wide discharge maintained that distribution (even if magnitude changed).

ds_ratio = ds['D_ROI'].loc[{'time': slice('1990-01-01','1999-12-31')}].sum(dim='time')
ds_ratio = ds_ratio / sum(ds_ratio)
# print(ds_ratio)

for r in ds['region']:
    # Set regional values to the average of the first 5 years when there is regional resolution
    ds['D_ROI'].sel({'region':r}).loc[{'time': slice('1840-01-01','1985-12-31')}] = ds['D'].loc[{'time': slice('1840-01-01','1985-12-31')}] * ds_ratio.sel(region=r.values).values
    # Set regional uncertainty to the full range of observed values
    errmax = ds['D_ROI'].sel({'region':r, 'time':slice('1986-01-01','1999-12-31')}).max()
    errmin = ds['D_ROI'].sel({'region':r, 'time':slice('1986-01-01','1999-12-31')}).min()
    ds['D_ROI_err'].sel({'region':r}).loc[{'time': slice('1840-01-01','1985-12-31')}] = (errmax-errmin)

ds = ds.transpose()

ds = ds.rename({'D_ROI':'discharge','D_ROI_err':'err'})
ds = ds.drop_vars('D')

ds['discharge'].attrs['units'] = 'Gt yr-1'
ds['err'].attrs['units'] = 'Gt yr-1'

ds['discharge'].attrs['long_name'] = 'Marine discharge. Includes both calving and submarine melt. Some calving is equivalent to submarine melt if using at fjord mouth'

ds = ds.sortby('region')
ds = ds.rename_dims({'region': 'region_name'})
ds = ds.rename({'region': 'region_name'})
ds = ds.set_index(region_name='region_name')

ds['region'] = (('region_name'),np.arange(7).astype(np.int32) + 1)
ds = ds.set_coords('region')
ds = ds.swap_dims({'region_name':'region'})
ds = ds.reset_coords('region_name')

ds['discharge'].attrs['standard_name'] = 'tendency_of_land_ice_mass_due_to_calving'
ds['discharge'].attrs['units'] = 'Gt yr-1'
ds['err'].attrs['standard_name'] = 'tendency_of_land_ice_mass_due_to_calving'

ds['time'].attrs['long_name'] = 'time'
ds['region'].attrs['long_name'] = 'Mouginot (2019) region'

ds.attrs['title'] = 'Greenland ice sheet discharge by Mouginot region'
ds.attrs['history'] = 'TBD'
ds.attrs['Conventions'] = 'CF-1.8'

ds.attrs['summary'] = 'Greenland ice sheet discharge by Mouginot region'
ds.attrs['creator_name'] = 'Ken Mankoff'
ds.attrs['creator_email'] = '[email protected]'
ds.attrs['institution'] = 'NASA GISS'
ds.attrs['references'] = 'TBD'
ds.attrs['DOI'] = 'https://doi.org/10.5281/zenodo.14020895'

comp = dict(zlib=True, complevel=5)
encoding = {} # var: comp for var in items}
encoding['time'] = {'dtype': 'i4'}

!rm ./dat/GL_discharge.nc
ds.to_netcdf('./dat/GL_discharge.nc', encoding=encoding)
!ncdump -h ./dat/GL_discharge.nc

netcdf GL_discharge {
dimensions:
	region = 7 ;
	time = 186 ;
variables:
	float discharge(region, time) ;
		discharge:_FillValue = NaNf ;
		discharge:long_name = "Marine discharge. Includes both calving and submarine melt. Some calving is equivalent to submarine melt if using at fjord mouth" ;
		discharge:standard_name = "tendency_of_land_ice_mass_due_to_calving" ;
		discharge:units = "Gt yr-1" ;
	float err(region, time) ;
		err:_FillValue = NaNf ;
		err:long_name = "Marine mass balance uncertainty" ;
		err:standard_name = "tendency_of_land_ice_mass_due_to_calving" ;
		err:units = "Gt yr-1" ;
	string region_name(region) ;
		region_name:long_name = "Mouginot 2019 regions" ;
	int time(time) ;
		time:cf_role = "timeseries_id" ;
		time:standard_name = "time" ;
		time:axis = "T" ;
		time:long_name = "time" ;
		time:units = "days since 1840-01-01 00:00:00" ;
		time:calendar = "proleptic_gregorian" ;
	int region(region) ;
		region:long_name = "Mouginot (2019) region" ;

// global attributes:
		:featureType = "timeSeries" ;
		:title = "Greenland ice sheet discharge by Mouginot region" ;
		:summary = "Greenland ice sheet discharge by Mouginot region" ;
		:keywords = "Greenland; Mass; Mass balance" ;
		:source = "git commit: 54a4452" ;
		:creator_name = "Ken Mankoff" ;
		:creator_email = "[email protected]" ;
		:creator_url = "http://kenmankoff.com" ;
		:institution = "NASA GISS" ;
		:references = "TBD" ;
		:product_version = 1. ;
		:history = "TBD" ;
		:Conventions = "CF-1.8" ;
		:DOI = "https://doi.org/10.5281/zenodo.14020895" ;
}
/tmp/ipykernel_613332/4182150838.py:47: UserWarning: rename 'region' to 'region_name' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.
  ds = ds.rename({'region': 'region_name'})