update_2023.org

Table of contents

• Introduction
• Summary of differences
• release_2022 vs release_2023 discharge
  • Extract time series
  • Graphics
    • r2023
• Compare using upstream basins
  • Recreate using ‘old’ data
  • Recreate using ‘new’ data

Introduction

This document shows changes between the release_2022 and release_2023 freshwater discharge product.

Changes to the code and manuscript can be seen at https://github.com/GEUS-Glaciology-and-Climate/freshwater/compare/release_2022…release_2023

Summary of differences

• Added 2022 RACMO data
• Added 2022 MAR data
• Updated MAR data from 3.12 to 3.13
• Updated BedMachine from v4 to v5
• Updated ArcticDEM from v3.0 to v4.1

release_2022 vs release_2023 discharge

Extract time series

mamba activate freshwater_user

python ./discharge.py --base="/home/kdm/data/Mankoff_2020/water/release_2022" --roi="-51.50,66.93 -51.21,66.74 -49.44,66.91 -49.84,67.18" -d -u > ./dat/r2022_roi.csv

python ./discharge.py --base="/home/kdm/data/Mankoff_2020/water/release_2023" --roi="-51.50,66.93 -51.21,66.74 -49.44,66.91 -49.84,67.18" -d -u > ./dat/r2023_roi.csv

Graphics

df_r2022 = pd.read_csv('./dat/r2022_roi.csv', index_col=0)
df_r2023 = pd.read_csv('./dat/r2023_roi.csv', index_col=0)

df_r2022 = df_r2022[['MAR_ice','RACMO_ice']]\
    .rename(columns={'MAR_ice':'MAR_ice_2022',
                     'RACMO_ice':'RACMO_ice_2022'})

df_r2023 = df_r2023[['MAR_ice','RACMO_ice']]\
    .rename(columns={'MAR_ice':'MAR_ice_2023',
                     'RACMO_ice':'RACMO_ice_2023'})

df = df_r2022.merge(df_r2023, left_index=True, right_index=True)

p = df.loc['2012-04-01':'2012-11-15']\
      .rolling(5)\
      .mean()\
      .plot(drawstyle='steps')

r2023

df = pd.read_csv('./dat/r2023_roi.csv', index_col=0)
df[['MAR_ice','RACMO_ice']]\
    .loc['2012-04-01':'2012-11-15']\
    .rolling(5)\
    .mean()\
    .plot(drawstyle='steps')

Note

graphics appear roughly the same, but the x-axis has nearly doubled.

Compare using upstream basins

Recreate using ‘old’ data

df = pd.read_csv('./dat/r2022_roi.csv', index_col=0)
p = df.loc['2012-04-01':'2012-11-15']\
      .rolling(5)\
      .mean()\
      .plot(drawstyle='steps')

Recreate using ‘new’ data

df = pd.read_csv('./dat/r2023_roi.csv', index_col=0)
p = df.loc['2012-04-01':'2012-11-15']\
      .rolling(5)\
      .mean()\
      .plot(drawstyle='steps')