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GL_basal_melt.org

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Introduction

Greenland meltwater profiles from

Data example

Printout

ncdump -h ./dat/GL_discharge.nc
netcdf GL_discharge {
dimensions:
	time = 185 ;
	region = 7 ;
variables:
	float discharge(time, region) ;
		discharge:_FillValue = NaNf ;
		discharge:long_name = "Marine discharge. Includes both calving and submarine melt. Some calvinvg is equivalent to submarine melt if using at fjord mouth" ;
		discharge:standard_name = "land_ice_mass_tranport" ;
		discharge:units = "Gt yr-1" ;
	float err(time, region) ;
		err:_FillValue = NaNf ;
		err:long_name = "Marine mass balance uncertainty" ;
		err:standard_name = "land_ice_mass_tranport" ;
		err:units = "Gt yr-1" ;
	string region(region) ;
		region:long_name = "Mouginot 2019 regions" ;
	int64 time(time) ;
		time:units = "days since 1840-01-01 00:00:00" ;
		time:calendar = "proleptic_gregorian" ;

// global attributes:
		:featureType = "timeSeries" ;
		:title = "Greenland ice sheet mass balance from 1840 through next week" ;
		:summary = "Greenland ice sheet mass balance from 1840 through next week" ;
		:keywords = "Greenland; Mass; Mass balance" ;
		:source = "git commit: 20af941" ;
		:creator_name = "Ken Mankoff" ;
		:creator_email = "kdm@geus.dk" ;
		:creator_url = "http://kenmankoff.com" ;
		:institution = "GEUS" ;
		:references = "10.22008/promice/mass_balance" ;
		:product_version = 1. ;
		:DOI = "https://doi.org/10.5281/zenodo.14020895" ;
}

Discharge by ROI

import xarray as xr
import matplotlib.pyplot as plt

ds = xr.open_dataset('dat/GL_discharge.nc').load()
df = ds['discharge'].to_dataframe()
ax = df['discharge'].unstack().plot(drawstyle='steps-post')
_ = ax.set_ylabel('Discharge [Gt yr$^{-1}$]')

./fig/GL_discharge_ROI.png

Fetch data

mkdir -p slater_2022
pushd slater_2022
zenodo_get https://zenodo.org/records/6498180
unzip -d . fjords_GRL_2022-v1.zip
pushd donaldaslater-fjords_GRL_2022-877eee7
popd

Reprocess

  • Annual discharge
  • Group by ROI
import xarray as xr
ds = xr.open_dataset('./tmp/greenland_discharge/MB_region.nc')

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

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

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

# Prior to 1986 there is no regional resolution, just one value for all of Greenland.
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_ROI'].sel({'region':r}).loc[{'time': slice('1986-01-01','1990-12-31')}].mean()
    # 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.rename({'D_ROI':'discharge','D_ROI_err':'err'})

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 calvinvg is equivalent to submarine melt if using at fjord mouth'
ds.attrs['DOI'] = 'https://doi.org/10.5281/zenodo.14020895'

!rm './dat/GL_discharge.nc'
ds.to_netcdf('./dat/GL_discharge.nc')
print(ds)

<xarray.Dataset> Size: 12kB
Dimensions:    (time: 185, region: 7)
Coordinates:
  * region     (region) <U2 56B 'NE' 'CE' 'SE' 'SW' 'CW' 'NW' 'NO'
  * time       (time) datetime64[ns] 1kB 1840-01-01 1841-01-01 ... 2024-01-01
Data variables:
    discharge  (time, region) float32 5kB 22.37 71.81 136.6 ... 46.07 10.38
    err        (time, region) float32 5kB 3.992 11.53 17.82 ... 9.919 1.912
Attributes:
    featureType:      timeSeries
    title:            Greenland ice sheet mass balance from 1840 through next...
    summary:          Greenland ice sheet mass balance from 1840 through next...
    keywords:         Greenland; Mass; Mass balance
    source:           git commit: 20af941
    creator_name:     Ken Mankoff
    creator_email:    kdm@geus.dk
    creator_url:      http://kenmankoff.com
    institution:      GEUS
    references:       10.22008/promice/mass_balance
    product_version:  1.0
    DOI:              https://doi.org/10.5281/zenodo.14020895