RATECE-PLANICA ski station (Slovenia) under CMIP-6 SSP585 condition

public 1yr ago Version: Version 1 0 bookmarks

View Workflow

ratece-planica-ski-station-slovenia-under-cmip-6-s — View Workflow

Description: SSP-based RCP scenario with high radiative forcing by the end of century. Following approximately RCP8.5 global forcing pathway with SSP5 socioeconomic conditions. Concentration-driven. Rationale: the scenario represents the high end of plausible future pathways. SSP5 is the only SSP with emissions high enough to produce the 8.5 W/m2 level of forcing in 2100.

This workflow is answering to the following scientific question:

Is it worth investing in artificial snowmaking equipment at RATECE-PLANICA?

Code Snippets

from ipyleaflet import Map, Marker, basemaps

center = (46.5, 13.70)

m = Map(
    basemap=basemaps.OpenTopoMap, 
    center=center,
    zoom=7
)
marker = Marker(location=center,  draggable=False)
m.add_layer(marker);
m

Jupyter Notebook From line 2 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

import xarray as xr
import pandas as pd
import cftime
import intake
from dask.distributed import Client
from dask.distributed import wait, progress
xr.set_options(display_style='html')
%matplotlib inline

Jupyter Notebook Pandas xarray dask cftime intake From line 17 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

filename = 'http://berkeleyearth.lbl.gov/auto/Stations/TAVG/Text/22498-TAVG-Data.txt'
data = pd.read_csv(filename, sep='\s+', header=None, comment='%')

Jupyter Notebook From line 28 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

mean_st_1961_2005 = data[(data[0] >= 1961 ) & (data[0] <= 2005)][6].mean()
std_st_1961_2005 = data[(data[0] >= 1961 ) & (data[0] <= 2005)][6].std()
print(mean_st_1961_2005 + 273.15, std_st_1961_2005)

Jupyter Notebook From line 33 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

# list of values 
lst = [mean_st_1961_2005 + 273.15, std_st_1961_2005] 

# Calling DataFrame constructor on list 
# with indices and columns specified 
df_st = pd.DataFrame(lst, index =['mean', 'stddev'], 
                                              columns =['RATECE-PLANICA-T2M-1961-2005']) 
df_st.to_csv('RATECE-PLANICA-1961-2005_t2m.csv')

Jupyter Notebook From line 39 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

col_url = "https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json"

Jupyter Notebook From line 54 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

58	col = intake.open_esm_datastore(col_url)

Jupyter Notebook From line 58 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

cat = col.search(experiment_id=['historical', 'ssp585'], table_id='day',
          variable_id=['tas', 'tasmin', 'tasmax'], source_id='CNRM-CM6-1-HR', institution_id='CNRM-CERFACS')

Jupyter Notebook From line 62 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

dset_dict = cat.to_dataset_dict(zarr_kwargs={'consolidated': True})
list(dset_dict.keys())

Jupyter Notebook From line 71 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

# Select 10 'workers' under 'manual scaling' menu below and click 'Scale'
# Click on the 'Dashboard link' to monitor calculation progress
from dask.distributed import Client
client = Client(n_workers=10, threads_per_worker=2, memory_limit='60GB')

Jupyter Notebook dask From line 76 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

ds_hist = dset_dict['CMIP.CNRM-CERFACS.CNRM-CM6-1-HR.historical.day.gr']
ds_hist

Jupyter Notebook From line 83 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

p = ds_hist['tas'].sel(time=slice(pd.datetime(1961, 1, 1), pd.datetime(2005, 12, 31))).sel(lat=46.5,lon=13.7, method="nearest")

Jupyter Notebook From line 88 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

mean_cmip6_1961_2005 = p.mean().values
std_cmip6_1961_2005 = p.std().values
print(mean_cmip6_1961_2005, std_cmip6_1961_2005)

Jupyter Notebook From line 96 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

# list of values 
lst = [mean_cmip6_1961_2005, std_cmip6_1961_2005] 

# Calling DataFrame constructor on list 
# with indices and columns specified 
df_cmip6 = pd.DataFrame(lst, index =['mean', 'stddev'], 
                                              columns =['RATECE-PLANICA-T2M-1961-2005']) 
df_cmip6.to_csv('RATECE-PLANICA-1961-2005_t2m_cmip6.csv')

Jupyter Notebook From line 102 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

p = ds_hist['tasmin'].sel(time=slice(pd.datetime(1961, 1, 1), pd.datetime(2010, 12, 31))).sel(lat=46.5,lon=13.7, method="nearest")
t2min_h_bias_corrected = (p - mean_cmip6_1961_2005)/std_cmip6_1961_2005 * std_st_1961_2005 + mean_st_1961_2005
t2min_h_bias_corrected

Jupyter Notebook From line 117 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

p = ds_hist['tasmax'].sel(time=slice(pd.datetime(1961, 1, 1), pd.datetime(2010, 12, 31))).sel(lat=46.5,lon=13.7, method="nearest")
t2max_h_bias_corrected = (p - mean_cmip6_1961_2005)/std_cmip6_1961_2005 * std_st_1961_2005 + mean_st_1961_2005
t2max_h_bias_corrected

Jupyter Notebook From line 123 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

ds_ssp585 = dset_dict['ScenarioMIP.CNRM-CERFACS.CNRM-CM6-1-HR.ssp585.day.gr']
ds_ssp585

Jupyter Notebook From line 129 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

p = ds_ssp585['tasmin'].sel(time=slice(pd.datetime(2021, 1, 1), pd.datetime(2100, 12, 31))).sel(lat=46.5,lon=13.7, method="nearest")
t2min_p_bias_corrected = (p - mean_cmip6_1961_2005)/std_cmip6_1961_2005 * std_st_1961_2005 + mean_st_1961_2005
t2min_p_bias_corrected

Jupyter Notebook From line 134 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

p = ds_ssp585['tasmax'].sel(time=slice(pd.datetime(2021, 1, 1), pd.datetime(2100, 12, 31))).sel(lat=46.5,lon=13.7, method="nearest")
t2max_p_bias_corrected = (p - mean_cmip6_1961_2005)/std_cmip6_1961_2005 * std_st_1961_2005 + mean_st_1961_2005
t2max_p_bias_corrected

Jupyter Notebook From line 140 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

nb_favourable_h = t2min_h_bias_corrected.where((t2min_h_bias_corrected < 0) & (t2max_h_bias_corrected <= 3)).groupby('time.year').count()

Jupyter Notebook From line 146 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

nb_favourable_p = t2min_p_bias_corrected.where((t2min_p_bias_corrected < 0) & (t2max_p_bias_corrected <= 3)).groupby('time.year').count()

Jupyter Notebook From line 154 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

162	nb_favourable = xr.merge([nb_favourable_h, nb_favourable_p])

Jupyter Notebook From line 162 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

170	series = nb_favourable.tasmin.to_series()

Jupyter Notebook From line 170 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

series = series.reset_index(level='member_id').drop(columns=['member_id'])

series.index = pd.to_datetime(series.index,  format='%Y')

Jupyter Notebook From line 174 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

184	series.to_csv("nb_favourable_snow_cmip6.csv")

Jupyter Notebook From line 184 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

188	series10YS = series.groupby(pd.Grouper(freq='10YS')).mean()

Jupyter Notebook From line 188 of raw/artificial_snow_cmip6_ssp585_Kranksja.ipynb

import matplotlib.pyplot as plt
from datetime import datetime
from datetime import timedelta
from dateutil.relativedelta import relativedelta

fig = plt.figure(1, figsize=[20,10])
ax = plt.subplot(1, 1, 1)
series10YS.plot.bar(ax=ax)
plt.axhline(y=30, color='r', linestyle='-', linewidth=3)
labels = [datetime.strptime(item.get_text(), '%Y-%m-%d %H:%M:%S').strftime("%Y") + '-' + 
            (datetime.strptime(item.get_text() , '%Y-%m-%d %H:%M:%S') +  relativedelta(years=9)).strftime("%Y")  for item in ax.get_xticklabels()] 
ax.set_xticklabels(labels, rotation=20, fontsize = 15)
ax.set_xlabel('Decade', fontsize = 20)
ax.set_ylabel('Average number of favourable days per year\n for the production of artifical snow', fontsize = 20)
plt.title("RATECE-PLANICA ski station (Slovenia) \n A dying ski tourism destination under SSP585 ", fontsize=30)
ax.legend(labels=['Threshold (30 days)','CNRM-CM6-1-HR'], fontsize=30)
plt.savefig("nb_favourable_snow_cmip6.png")