"""

Class that is passed to the CMIP6 calculations containing the configuration.

"""

def __init__(self):

"""

This function initialized the configuration for the CMIP6 calculations.

"""

logging.info("[CMIP6_config] Defining the config file for the calculations")

self.fs = gcsfs.GCSFileSystem(token="anon", access="read_only")

self.grid_labels = ["gn"] # Can be gr=grid rotated, or gn=grid native

# FOR RTM we are using:

# "CMCC-ESM2": ["r1i1p2f1"]

# "CanESM5": ["r1i1p2f1","r2i1p2f1","r9i1p2f1","r10i1p2f1","r7i1p2f1","r6i1p2f1","r3i1p2f1"]

# "MPI-ESM1-2-LR": ["r10i1p1f1","r1i1p1f1","r4i1p1f1","r2i1p1f1","r6i1p1f1"]

# "UKESM1-0-LL": ["r1i1p1f2","r2i1p1f2","r3i1p1f2","r4i1p1f2","r8i1p1f2"]

# "MPI-ESM1-2-HR": ["r1i1p1f1","r2i1p1f1"]

self.experiment_ids = ["ssp585"] #,"ssp585"]

self.source_id = None

self.member_id = None

self.variable_ids = [

"prw",

"clt",

"uas",

"vas",

"chl",

"sithick",

"siconc",

"sisnthick",

"sisnconc",

"tas",

"tos",

] # ,"toz"]

self.table_ids = [

"Amon",

"Amon",

"Amon",

"Amon",

"Omon",

"SImon",

"SImon",

"SImon",

"SImon",

"Amon",

"Omon",

]

self.bias_correct_ghi = True

self.bias_correct_file = "bias_correct/ghi_deltas.nc"

self.sensitivity_run = False

self.dset_dict = {}

self.start_date = "1979-01-01"

self.end_date = "2099-12-16"

if self.sensitivity_run:

# For sensitivity runs we do 40 year periods to

# evaluate the sensitivity from individual factors.

self.end_date = "1989-12-16"

self.scenarios = ["osa", "no_ice", "no_chl", "no_wind", "no_osa", "no_meltpond", "snow_sensitivity"]

# self.scenarios = ["osa","snow_sensitivity"]

else:

self.scenarios = ["osa"]

self.use_local_CMIP6_files = True

self.write_CMIP6_to_file = False

self.perform_light_calculations = True

self.cmip6_netcdf_dir = "light"

self.cmip6_outdir = "light/nfiles"

if self.sensitivity_run:

self.cmip6_outdir = "light_sensitivity"

if os.path.exists(self.cmip6_outdir):

os.makedirs(self.cmip6_outdir, exist_ok=True)

# Cut the region of the global data to these longitude and latitudes

if self.write_CMIP6_to_file:

# We want to save the entire northern hemisphere for possible use later

# while calculations are done north of 50N

self.min_lat = 0

self.start_date = "1950-01-01"

else:

self.min_lat = 60

self.max_lat = 85

self.min_lon = 0

self.max_lon = 360

# ESMF and Dask related

self.interp = "bilinear"

self.outdir = f"/mnt/disks/actea-disk-1/{self.cmip6_outdir}"

if os.path.exists(self.outdir):

os.makedirs(self.outdir, exist_ok=True)

self.selected_depth = 0

self.models = {}

# Define the range of wavelengths that constitue the different parts of the spectrum.

self.start_index_uv = len(np.arange(200, 200, 10))

self.end_index_uv = len(np.arange(200, 410, 10))

self.start_index_uvb = len(np.arange(200, 280, 10))

self.end_index_uvb = len(np.arange(200, 320, 10))

self.start_index_uva = len(np.arange(200, 320, 10))

self.end_index_uva = len(np.arange(200, 400, 10))

self.start_index_visible = len(np.arange(200, 400, 10))

self.end_index_visible = len(np.arange(200, 710, 10))

self.start_index_nir = len(np.arange(200, 800, 10))

self.end_index_nir = len(np.arange(200, 2500, 10))

self.setup_erythema_action_spectrum()

def setup_logging(self):

logger = logging.getLogger()

logger.setLevel(logging.INFO)

def read_cmip6_repository(self):

self.df = pd.read_csv(

"https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv"

)

def setup_parameters(self):

wl = pd.read_csv(

"data/Wavelength/Fresnels_refraction.csv", header=0, sep=";", decimal=","

)

self.wavelengths = wl["λ"].values

self.refractive_indexes = wl["n(λ)"].values

self.alpha_chl = wl["a_chl(λ)"].values

self.alpha_w = wl["a_w(λ)"].values

self.beta_w = wl["b_w(λ)"].values

self.alpha_wc = wl["a_wc(λ)"].values

self.solar_energy = wl["E(λ)"].values

self.fractions_shortwave_uv = self.solar_energy[self.start_index_uv:self.end_index_uv]

self.fractions_shortwave_vis = self.solar_energy[

self.start_index_visible:self.end_index_visible

]

self.fractions_shortwave_nir = self.solar_energy[self.start_index_nir:self.end_index_nir]

logging.info(

"[CMIP6_config] Energy fraction UV ({} to {}): {:3.3f}".format(

self.wavelengths[self.start_index_uv],

self.wavelengths[self.end_index_uv],

np.sum(self.fractions_shortwave_uv),

)

)

logging.info(

"[CMIP6_config] Energy fraction PAR ({} to {}): {:3.3f}".format(

self.wavelengths[self.start_index_visible],

self.wavelengths[self.end_index_visible],

np.sum(self.fractions_shortwave_vis),

)

)

# logging.info("[CMIP6_config] Energy fraction NIR ({} to {}): {:3.3f}".format(self.wavelengths[self.start_index_nir],

# self.wavelengths[self.end_index_nir],

# np.sum(

# self.fractions_shortwave_nir)))

# Read in the ice parameterization for how ice absorbs irradiance as a function of wavelength.

# Based on Perovich 1996

ice_wl = pd.read_csv(

"ice-absorption/sea_ice_absorption_perovich_and_govoni_interpolated.csv",

header=0,

sep=",",

decimal=".",

)

self.wavelengths_ice = ice_wl["wavelength"].values

self.absorption_ice_pg = ice_wl["k_ice_pg"].values

def setup_erythema_action_spectrum(self):

# Spectrum suggested by:

# A.F. McKinlay, A.F. and B.L. Diffey,

# "A reference action spectrum for ultraviolet induced erythema in human skin",

# CIE Research Note, 6(1), 17-22, 1987

# https://www.esrl.noaa.gov/gmd/grad/antuv/docs/version2/doserates.CIE.txt

# A = 1 for 250 <= W <= 298

# A = 10^(0.094(298- W)) for 298 < W < 328

# A = 10^(0.015(139-W-)) for 328 < W < 400

wavelengths = np.arange(200, 410, 10)

self.erythema_spectrum = np.zeros(len(wavelengths))

# https://www.nature.com/articles/s41598-018-36850-x

for i, wavelength in enumerate(wavelengths):

if 250 <= wavelength <= 298:

self.erythema_spectrum[i] = 1.0

elif 298 <= wavelength <= 328:

self.erythema_spectrum[i] = 10.0 ** (0.094 * (298 - wavelength))

elif 328 < wavelength < 400:

self.erythema_spectrum[i] = 10.0 ** (0.015 * (139 - wavelength))

logging.info(

"[CMIP6_config] Calculated erythema action spectrum for wavelengths 290-400 at 10 nm increment"

)

def setup_ozone_uv_spectrum(self):

# Data collected from Figure 4

# http://html.rhhz.net/qxxb_en/html/20190207.htm#rhhz

infile = "ozone-absorption/O3_UV_absorption_edited.csv"

df = pd.read_csv(infile, sep="\t")

# Get values from dataframe

o3_wavelength = df["wavelength"].values

o3_abs = df["o3_absorption"].values

wavelengths = np.arange(200, 410, 10)

# Do the linear interpolation

o3_abs_interp = np.interp(wavelengths, o3_wavelength, o3_abs)

logging.info(

"[CMIP6_config] Calculated erythema action spectrum for wavelengths 290-400 at 10 nm increment"

)

return o3_abs_interp, wavelengths

def setup_absorption_chl(self):

# Data exported from publication Matsuoka et al. 2007 (Table. 3)

# Data are interpolated to a fixed wavelength grid that fits with the wavelengths of

# Seferian et al. 2018

infile = "chl-absorption/Matsuoka2007-chla_wavelength_absorption.csv"

df = pd.read_csv(infile, sep=" ")

# Get values from dataframe

chl_abs_A = df["A"].values

chl_abs_B = df["B"].values

chl_abs_wavelength = df["wavelength"].values

# Interpolate to 10 nm wavelength bands - only visible

# This is because all other wavelength calculations are done at 10 nm bands.

# Original Matsuoka et al. 2007 operates at 5 nm bands.

wavelengths = np.arange(400, 710, 10)

# Do the linear interpolation

A_chl_interp = np.interp(wavelengths, chl_abs_wavelength, chl_abs_A)

B_chl_interp = np.interp(wavelengths, chl_abs_wavelength, chl_abs_B)

return A_chl_interp, B_chl_interp, wavelengths

# import matplotlib.pyplot as plt

# plt.plot(self.wavelengths,self.solar_energy)

# plt.title("Energy contributions from wavelengths 200-4000")