This document shows changes between the v2 and v3 freshwater discharge product.
Changes to the code and manuscript can be seen by viewing the difference between the latest (eventually: v3 tag) and the earlier (v2 tag) of the git repository on GitHub (insert link).
The data product has changed for the following reasons:
- Upgrade BedMachine from v3 to v4
- Upgrade regional climate model (RCM) inputs
The BedMachine upgrade can lead to significant regional changes, because upstream subglacial ice basins may change their boundaries.
By chance, the arbitrary box in the README.org used to demonstrate how to to use the data and provided code interface has a significant change, nearly doubling the discharge into this box. Here we explore why, and perform some quality control. We expect the neighboring basin should decrease discharge by roughly the amount that this basin increases discharge.
The original README example discharge graphic is re-created here, except we use the command-line interface rather than the Python interface to collect the data, then plot and compare.
df = pd.read_csv('./dat/old_roi.csv', index_col=0)
df[['MAR_ice','RACMO_ice']]\
.loc['2012-04-01':'2012-11-15']\
.rolling(5)\
.mean()\
.plot(drawstyle='steps')
savefig('./old_roi.png', bbox_inches='tight')
- Although the colors are different, the data in the plot appears to be exactly the same.
df = pd.read_csv('./dat/new_roi.csv', index_col=0)
df[['MAR_ice','RACMO_ice']]\
.loc['2012-04-01':'2012-11-15']\
.rolling(5)\
.mean()\
.plot(drawstyle='steps')
savefig('./new_roi.png', bbox_inches='tight')
- Note
- graphics appear roughly the same, but the x-axis has nearly doubled.
The easiest way to view the changes between the two graphics below is to open each in a new browser tab or window, and then use your keyboard to toggle back-and-forth between the two.
df = pd.read_csv('./dat/old_roi.csv', index_col=0)
df.loc['2012-04-01':'2012-11-15']\
.rolling(5)\
.mean()\
.plot(drawstyle='steps')
savefig('./old_all.png', bbox_inches='tight')
df = pd.read_csv('./dat/new_roi.csv', index_col=0)
df.loc['2012-04-01':'2012-11-15']\
.rolling(5)\
.mean()\
.plot(drawstyle='steps')
savefig('./new_all.png', bbox_inches='tight')