AQ_calving.org

Table of contents

• Introduction
  • Data example
    • Printout
    • Graphic
    • Calving by region
• Processing

Introduction

• Antarctic ice shelf calving is from Davison 2023
• Antarctic non-shelf calving is the difference between Davison 2023 and Rignot 2019
  • Should be updated to Davison ESSD 2023 submitted

Data example

Printout

xr.open_dataset('./dat/AQ_calving.nc')

<xarray.Dataset> Size: 8kB
Dimensions:      (region: 18, time: 25)
Coordinates:
  * time         (time) datetime64[ns] 200B 1997-07-01 1998-07-01 ... 2021-07-01
  * region       (region) int32 72B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Data variables:
    calving      (region, time) float64 4kB ...
    uncertainty  (region, time) float64 4kB ...
    region_name  (region) <U5 360B ...
Attributes:
    description:   Antarctic region ice shelf calving rate
    date_created:  20241101T153743Z
    title:         Calving per region
    history:       Processed for Schmidt (YYYY; in prep); by Ken Mankoff
    source:        doi:10.5281/ZENODO.8052519
    Conventions:   CF-1.8
    DOI:           https://doi.org/10.5281/zenodo.14020895

Graphic

import xarray as xr
ds = xr.open_dataset('./dat/AQ_calving.nc')
df = ds.sum(dim='region')['calving'].to_dataframe()
ax = df['calving'].plot(drawstyle='steps-post')

unc = (((ds['uncertainty']**2).sum(dim='region'))**0.5).to_dataframe()
unc.plot(drawstyle='steps-post', ax=ax)
_ = ax.set_ylabel('Calving [Gt yr$^{-1}$]')

Calving by region

import xarray as xr
import matplotlib.pyplot as plt

ds = xr.open_dataset('dat/AQ_calving.nc')
ds = ds.assign_coords(region=[_[0] + ' ['+str(_[1])+']' for _ in zip(ds['region_name'].values,ds['region'].values)])
df = ds['calving'].to_dataframe()
df = df['calving'].unstack().T
df['AQ'] = df.sum(axis='columns')
df.loc['Mean'] = df.mean(axis='rows')
df.index = [_[0:10] for _ in df.index.astype(str)]
df.round()

	A-Ap [1]	Ap-B [2]	B-C [3]	C-Cp [4]	Cp-D [5]	D-Dp [6]	Dp-E [7]	E-Ep [8]	Ep-F [9]	F-G [10]	G-H [11]	H-Hp [12]	Hp-I [13]	I-Ipp [14]	Ipp-J [15]	J-Jpp [16]	Jpp-K [17]	K-A [18]	AQ
1997-07-01	56	37	42	93	141	114	26	43	108	83	200	37	48	45	10	139	93	48	1363
1998-07-01	56	37	42	93	141	114	26	43	108	83	200	37	48	45	10	1995	93	48	3219
1999-07-01	56	37	42	93	141	114	26	43	108	83	200	37	48	45	10	139	93	48	1363
2000-07-01	1	13	0	11	53	304	28	0	2944	19	129	22	215	245	68	2064	0	11	6125
2001-07-01	105	227	11	179	296	170	114	129	134	302	475	67	138	75	37	153	102	61	2776
2002-07-01	30	17	0	90	62	34	10	13	1	34	230	21	43	436	1	0	8	2	1033
2003-07-01	174	20	2	27	91	42	10	1058	0	51	176	23	33	27	1	0	10	3	1750
2004-07-01	104	55	64	155	178	135	42	68	108	130	289	48	83	64	11	15	48	40	1636
2005-07-01	15	21	2	24	109	28	18	0	25	52	60	34	46	347	2	0	37	26	850
2006-07-01	69	28	0	49	155	72	36	1	9	94	102	27	38	162	5	41	7	17	910
2007-07-01	19	27	11	30	173	80	34	9	38	85	103	40	42	12	3	0	10	24	739
2008-07-01	21	38	0	33	152	37	12	0	1	17	58	47	286	33	4	0	0	4	744
2009-07-01	61	51	31	94	204	136	48	17	76	147	1670	71	93	58	8	85	69	55	2974
2010-07-01	12	46	6	111	128	614	21	6	32	79	230	81	255	36	2	0	17	45	1722
2011-07-01	45	22	0	48	191	64	20	0	9	76	92	64	71	11	3	0	0	25	741
2012-07-01	3	20	12	48	121	74	21	0	20	97	180	39	54	4	4	0	14	10	722
2013-07-01	6	20	0	57	121	38	10	0	2	34	698	61	67	3	3	0	4	5	1130
2014-07-01	26	37	4	163	163	68	21	5	20	67	488	93	80	52	13	14	33	26	1374
2015-07-01	78	26	0	88	56	43	67	0	3	148	220	71	107	13	3	5	0	57	986
2016-07-01	37	20	0	42	116	29	19	7	10	51	302	34	49	14	2	26	9	25	791
2017-07-01	66	82	0	54	176	77	45	0	9	152	307	34	49	14	3	9	4	22	1102
2018-07-01	31	48	16	65	216	52	21	0	10	107	207	35	50	1325	3	11	2	20	2219
2019-07-01	25	36	0	60	126	43	20	6	3	89	361	36	43	32	4	6	7	21	917
2020-07-01	19	36	367	56	153	111	52	0	8	116	210	28	43	50	4	0	0	15	1269
2021-07-01	49	39	2	143	253	500	27	4	27	127	292	31	66	23	109	1019	7	176	2895
Mean	47	42	26	76	149	124	31	58	153	93	299	45	84	127	13	229	27	33	1654

Processing

import numpy as np
import pandas as pd
import geopandas as gpd
import xarray as xr
import datetime

# shelf name with longitude and latitude
df = pd.read_excel("~/data/Davison_2023/adi0186_table_s2.xlsx",
                   sheet_name = 'Total mass changes',
                   usecols = (1,2,3), index_col = 0, skiprows = 4)
df = df.dropna()
shelf = gpd.GeoDataFrame(
    geometry=gpd.points_from_xy(df.longitude, df.latitude, crs="EPSG:4326"), data=df)
shelf = shelf.to_crs('EPSG:3031')

# region name
region = gpd.read_file("~/data//IMBIE/Rignot/ANT_Basins_IMBIE2_v1.6.shp")
region = region[region['Regions'] != 'Islands']

# find regions nearest each shelf
shelf_region = gpd.sjoin_nearest(shelf,region)
shelf_region = shelf_region.drop(columns=['index_right','latitude','longitude','Regions'])

# load calving time series per shelf
calving = pd.read_excel("~/data/Davison_2023/adi0186_table_s2.xlsx",
                        sheet_name='Calving', index_col=1, skiprows=3, header=(0,1))
calving = calving.T.dropna().drop(columns=['Antarctic Ice Shelves'])

# WARNING: Calving < 0 implies error is baseline rate. This happens fairly often (small values) and occasionally (large values)
calving[calving < 0] = 0

obs = calving.xs('observed', level='Ice shelf')
obs.index.name = 'Date'
obs.index = pd.to_datetime(obs.index.astype(int).astype(str)+'-07-01', format="%Y-%m-%d")

# unc = calving.drop('observed', level=1, axis=0).reset_index().set_index('level_0').drop(columns=['Ice shelf'])
unc = calving.xs('uncertainty', level='Ice shelf')
unc.index.name = 'Date'
unc.index = obs.index

da_obs = xr.DataArray(data = obs.values,
                      dims = ['date','shelf'],
                      coords = {'date':obs.index.values, 'shelf':obs.columns})

ds = xr.Dataset({'calving': da_obs})
ds['uncertainty'] = (('date','shelf'), unc)
ds = ds.where(ds['shelf'] != 'Antarctic Ice Shelves', drop=True)
ds['region'] = (('shelf'), shelf_region['Subregion'])

# ds = ds.groupby('region').sum() # Want to agg() with different functions per column...

# uncertainty is sqrt of sum of squares. Not sure how to do this in-place in Xarray.
ds['unc2'] = ds['uncertainty']**2
ds2 = xr.merge([
    ds[['calving','region']].groupby('region').sum(),
    ds[['unc2','region']].groupby('region').sum(),
])
ds2['uncertainty'] = np.sqrt(ds2['unc2'])
ds2 = ds2.drop_vars('unc2')
# uncertainty for all of AQ as (sum(u**2))**0.5 matches Davison 2023 row 168 "Antarctic Ice Shelves"

# need to calculate AQ-wide uncertainty at shelf resolution because step-aggregating is not commutative
# ds2['uncertainty_AQ'] = np.sqrt(ds['unc2'].sum(dim='shelf'))

ds = ds2

ds = ds.rename({'date':'time'})
ds['region'] = np.arange(18).astype(np.int32) + 1

ds['region_name'] = (('region'), ['A-Ap', 'Ap-B', 'B-C', 'C-Cp', 'Cp-D',
                                'D-Dp', 'Dp-E', 'E-Ep', 'Ep-F', 'F-G',
                                'G-H', 'H-Hp', 'Hp-I', 'I-Ipp', 'Ipp-J',
                                'J-Jpp', 'Jpp-K', 'K-A'])

ds.attrs['description'] = 'Antarctic region ice shelf calving rate'
ds['calving'].attrs['units'] = 'Gt yr-1'
ds['calving'].attrs['long_name'] = 'Shelf calving'

# ds['calving'].attrs['standard_name'] = 'water_flux_into_sea_water_from_land_ice'
# https://github.com/orgs/cf-convention/discussions/388
ds['calving'].attrs['standard_name'] = 'ice_transport_across_line'

ds['uncertainty'].attrs['long_name'] = 'Uncertainty of shelf calving'
ds['time'].attrs['standard_name'] = 'time'
ds['region'].attrs['long_name'] = 'IMBIE region'
ds.attrs['date_created'] = datetime.datetime.now(datetime.timezone.utc).strftime("%Y%m%dT%H%M%SZ")
ds.attrs['title'] = 'Calving per region'
ds.attrs['history'] = 'Processed for Schmidt (YYYY; in prep); by Ken Mankoff'
ds.attrs['source'] = 'doi:10.5281/ZENODO.8052519'
ds.attrs['Conventions'] = 'CF-1.8'
ds.attrs['DOI'] = 'https://doi.org/10.5281/zenodo.14020895'

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

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

netcdf AQ_calving {
dimensions:
	region = 18 ;
	time = 25 ;
variables:
	double calving(region, time) ;
		calving:_FillValue = NaN ;
		calving:units = "Gt yr-1" ;
		calving:long_name = "Shelf calving" ;
		calving:standard_name = "ice_transport_across_line" ;
	int time(time) ;
		time:standard_name = "time" ;
		time:units = "days since 1997-07-01 00:00:00" ;
		time:calendar = "proleptic_gregorian" ;
	int region(region) ;
		region:long_name = "IMBIE region" ;
	double uncertainty(region, time) ;
		uncertainty:_FillValue = NaN ;
		uncertainty:long_name = "Uncertainty of shelf calving" ;
	string region_name(region) ;

// global attributes:
		:description = "Antarctic region ice shelf calving rate" ;
		:date_created = "20241101T153743Z" ;
		:title = "Calving per region" ;
		:history = "Processed for Schmidt (YYYY; in prep); by Ken Mankoff" ;
		:source = "doi:10.5281/ZENODO.8052519" ;
		:Conventions = "CF-1.8" ;
		:DOI = "https://doi.org/10.5281/zenodo.14020895" ;
}