"""
This file is part of CLIMADA.
Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
CLIMADA is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free
Software Foundation, version 3.
CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
---
Define climate change scenarios for tropical cycones.
"""
import numpy as np
import pandas as pd
from climada.util.constants import SYSTEM_DIR
TOT_RADIATIVE_FORCE = SYSTEM_DIR.joinpath('rcp_db.xls')
"""© RCP Database (Version 2.0.5) http://www.iiasa.ac.at/web-apps/tnt/RcpDb.
generated: 2018-07-04 10:47:59."""
[docs]
def get_knutson_criterion():
"""
Fill changes in TCs according to Knutson et al. 2015 Global projections
of intense tropical cyclone activity for the late twenty-first century from
dynamical downscaling of CMIP5/RCP4.5 scenarios.
Returns
-------
criterion : list(dict)
list of the criterion dictionary for frequency and intensity change
per basin, per category taken from the Table 3 in Knutson et al. 2015.
with items 'basin' (str), 'category' (list(int)), 'year' (int),
'change' (float), 'variable' ('intensity' or 'frequency')
"""
# NA
na = [
{'basin': 'NA', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NA', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.045, 'variable': 'intensity'}
]
# EP
ep = [
{'basin': 'EP', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1.163, 'variable': 'frequency'},
{'basin': 'EP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.193, 'variable': 'frequency'},
{'basin': 'EP', 'category': [3, 4, 5],
'year': 2100, 'change': 1.837, 'variable': 'frequency'},
{'basin': 'EP', 'category': [4, 5],
'year': 2100, 'change': 3.375, 'variable': 'frequency'},
{'basin': 'EP', 'category': [0],
'year': 2100, 'change': 1.082, 'variable': 'intensity'},
{'basin': 'EP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.078, 'variable': 'intensity'}
]
# WP
wp = [
{'basin': 'WP', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.345, 'variable': 'frequency'},
{'basin': 'WP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.316, 'variable': 'frequency'},
{'basin': 'WP', 'category': [3, 4, 5],
'year': 2100, 'change': 1 - 0.169, 'variable': 'frequency'},
{'basin': 'WP', 'category': [4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'WP', 'category': [0],
'year': 2100, 'change': 1.074, 'variable': 'intensity'},
{'basin': 'WP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.055, 'variable': 'intensity'},
]
# SP
sp = [
{'basin': 'SP', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.366, 'variable': 'frequency'},
{'basin': 'SP', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.406, 'variable': 'frequency'},
{'basin': 'SP', 'category': [3, 4, 5],
'year': 2100, 'change': 1 - 0.506, 'variable': 'frequency'},
{'basin': 'SP', 'category': [4, 5],
'year': 2100, 'change': 1 - 0.583, 'variable': 'frequency'}
]
# NI
ni = [
{'basin': 'NI', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NI', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.256, 'variable': 'frequency'},
{'basin': 'NI', 'category': [3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'NI', 'category': [4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'}
]
# SI
si = [
{'basin': 'SI', 'category': [0, 1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.261, 'variable': 'frequency'},
{'basin': 'SI', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1 - 0.284, 'variable': 'frequency'},
{'basin': 'SI', 'category': [3, 4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'SI', 'category': [4, 5],
'year': 2100, 'change': 1, 'variable': 'frequency'},
{'basin': 'SI', 'category': [1, 2, 3, 4, 5],
'year': 2100, 'change': 1.033, 'variable': 'intensity'}
]
return na + ep + wp + sp + ni + si
[docs]
def calc_scale_knutson(ref_year=2050, rcp_scenario=45):
"""
Comparison 2081-2100 (i.e., late twenty-first century) and 2001-20
(i.e., present day). Late twenty-first century effects on intensity and
frequency per Saffir-Simpson-category and ocean basin is scaled to target
year and target RCP proportional to total radiative forcing of the
respective RCP and year.
Parameters
----------
ref_year : int, optional
year between 2000 ad 2100. Default: 2050
rcp_scenario: int, optional
26 for RCP 2.6, 45 for RCP 4.5. The default is 45
60 for RCP 6.0 and 85 for RCP 8.5.
Returns
-------
factor : float
factor to scale Knutson parameters to the give RCP and year
"""
# Parameters used in Knutson et al 2015
base_knu = np.arange(2001, 2021)
end_knu = np.arange(2081, 2101)
rcp_knu = 45
# radiative forcings for each RCP scenario
rad_force = pd.read_excel(TOT_RADIATIVE_FORCE)
years = np.array([year for year in rad_force.columns if isinstance(year, int)])
rad_rcp = np.array([int(float(sce[sce.index('.') - 1:sce.index('.') + 2]) * 10)
for sce in rad_force.Scenario if isinstance(sce, str)])
# mean values for Knutson values
rf_vals = np.argwhere(rad_rcp == rcp_knu).reshape(-1)[0]
rf_vals = np.array([rad_force.iloc[rf_vals][year] for year in years])
rf_base = np.nanmean(np.interp(base_knu, years, rf_vals))
rf_end = np.nanmean(np.interp(end_knu, years, rf_vals))
# scale factor for ref_year and rcp_scenario
rf_vals = np.argwhere(rad_rcp == rcp_scenario).reshape(-1)[0]
rf_vals = np.array([rad_force.iloc[rf_vals][year] for year in years])
rf_sel = np.interp(ref_year, years, rf_vals)
return max((rf_sel - rf_base) / (rf_end - rf_base), 0)
