Commit ab25a965 authored by Lana B's avatar Lana B
Browse files

new functions added:

- remove_incomplete_timeseries(amoc_index): removes members that are incomplete
- get_amoc_strengths(amoc_index): returns dataset with AMOC strength at 26.5N and at 35N
parent 01199bf5
......@@ -10,6 +10,7 @@ from mpl_toolkits.axes_grid1 import make_axes_locatable
import as ccrs
import os
import glob
import json
import xarray as xr
......@@ -111,6 +112,9 @@ def get_subpolar_gyre(model, emember, print_info=False, paths_all_models=None):
return None
data = xr.open_mfdataset(paths[0], concat_dim="time").tos
if model == 'CNRM-CM6-1-HR':
# this crashes with a oom-kill, so re-chunking with 2-timestep-chunks seems to help...
data = data.chunk(dict(time=2))
str_lon = get_lon_lat_keys(data, lon_keys)
str_lat = get_lon_lat_keys(data, lat_keys)
......@@ -127,6 +131,72 @@ def get_subpolar_gyre(model, emember, print_info=False, paths_all_models=None):
return spg
# set everything to nan where there are no full timeseries for the AMOC strength at 26.5N and 35N
def remove_incomplete_timeseries(amoc_index):
amoc = get_amoc_strengths(amoc_index)
amoc_strength_265 = amoc.amoc_strength_265.astype(np.float)
amoc_strength_35 = amoc.amoc_strength_35.astype(np.float)
incomplete = (np.isnan(amoc_strength_265).sum(dim="year") > 0) & (np.isnan(amoc_strength_35).sum(dim="year") > 0 )
for m in range(len(amoc_strength_265.models.values)):
for em in range(len(amoc_strength_265.ensemble_members.values)):
if incomplete.isel(models=m, ensemble_members=em):
amoc_strength_265.isel(models=m, ensemble_members=em).values.fill(np.nan)
amoc_strength_35.isel(models=m, ensemble_members=em).values.fill(np.nan)
amoc_index.isel(models=m, ensemble_members=em).values.fill(np.nan)
# remove the UKESM1-0-LL r1i1p1f1 in the amoc-strength data
model = 'UKESM1-0-LL'
emember = 'r1i1p1f1'
amoc_strength_265.sel(models = model, ensemble_members = emember).values.fill(np.nan)
amoc_strength_35.sel(models = model, ensemble_members = emember).values.fill(np.nan)
# save all CMIP6 amoc data in one file
strength_265N = amoc_strength_265,
strength_35N = amoc_strength_35,
index = amoc_index.groupby('time.year').mean(dim="time")
)).to_netcdf("", mode='w')
return amoc_index
# getMatthew's AMOC strength
def get_amoc_strengths(amoc_index):
with open('matthew/JSON_data/Figure_AR6_CMIP5-6_AMOC_35N_1000m.json', 'r') as handle:
json_load = json.load(handle)
amoc_c6_ts =["amoc_c6_ts"])
cmip6_models = json_load["cmip6_models"]
year = np.asarray(json_load["year"])
experiments = ['ssp119', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
latitudes = ['26.5N', '35N']
Ensemble members:
These are the first 10 ensemble members r${ens_num}i1p1f1 in the respective experiments.
Where "f1" was not available we have used "f2" or "f3" and so on.
amoc_strength = xr.DataArray(
dims = ('models', 'experiments', 'ensemble_members', 'latitudes', 'year'),
coords = dict(
models = xr.DataArray(cmip6_models, dims="models", coords=dict(models=("models", cmip6_models))),
experiments = xr.DataArray(experiments, dims="experiments", coords=dict(experiments=("experiments", experiments))),
latitudes = xr.DataArray(latitudes, dims="latitudes", coords=dict(latitudes=("latitudes", latitudes))),
year = xr.DataArray(year, dims="year", coords=dict(year=("year", year))),
ensemble_members = amoc_index.ensemble_members
amoc_index = amoc_index.groupby('time.year').mean(dim="time")
# the historical period is same over all experiments, so just choose any
return xr.Dataset(
amoc_strength_265 = amoc_strength.sel(latitudes='26.5N', experiments='ssp119', drop=True).sel(year=amoc_index.year),
amoc_strength_35 = amoc_strength.sel(latitudes='35N', experiments='ssp119', drop=True).sel(year=amoc_index.year)
def create_new_amoc_index_file(output_file):
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