Visually Comparing Climate Models¶

Summary¶

In this notebook, I will be comparing three climate reanalysis models:

NCEP-DOE Reanalysis 2: Surface
ERA5
CMIP5

I will be looking at the following variables:

Mean Sea Level Pressure (CMIP5, ERA5, NCEP)
Surface Pressure (ERA5, NCEP)

I will be trying to user RBIG in order to assess how similar these models are. I'll be looking at the following IT measures. If these climate models are that similar, then they should exhibit similar IT measures.

Preprocessing Steps¶

Regridded Spatially

The ERA5 had the coarsest spatial resolution (2.5 x 2.5).
I regridded the NCEP from (0.25 x 0.25) to (2.5 x 2.5).
I regridded the CMIP5 from (2 x 2.5) to (2.5 x 2.5).

Temporal Resolution

ERA5 and NCEP go from 1980-2019
CMIP5 goes from 2006-2018
For comparing ERA5 vs CMIP5 and NCEP vs CMIPF, I found the same time components

Measures¶

I'm measuring the following:

Entropy - expected uncertainty
Total Correlation - amount of redundant information between features
Mutual Information - amount of information shared between variables

Data¶

Inputs

I'm taking each year as is. Each spatial location is a sample and each year is a feature. My inputs are:

$X \in \mathbb{R}^{\text{spatial } \times \text{ month}}$

Outputs

All my information theory measures are in nats. They are scalars.

Hypothesis¶

Simple: The ERA5 and the NCEP model should be more similar than the CMIP5 model compared to each of them.

Data - Climate Models¶

import os, sys
cwd = os.getcwd()
source_path = f"{cwd}/../../"
sys.path.insert(0, f'{source_path}')

# Import RBIG Helper
from src.models.train_models import run_rbig_models

# ESDC tools
sys.path.insert(0, f'/home/emmanuel/code/py_esdc')
# from esdc.preprocessing import normalize_temporal

import cdsapi
from zipfile import ZipFile
import pandas as pd
import xarray as xr
from tqdm import tqdm
from sklearn import preprocessing

# Visualization Tools
from src.data.climate.loader import ResultsLoader
from src.visualization.climate import PlotResults

import seaborn as sns
import matplotlib.pyplot as plt
# plt.style.use('ggplot')
plt.style.use(['seaborn-poster', 'fivethirtyeight'])

%matplotlib inline

%load_ext autoreload
%autoreload 2

data_path = f"/home/emmanuel/projects/2020_rbig_rs/data/climate/raw/"
results_path = f"/home/emmanuel/projects/2020_rbig_rs/data/climate/results/"
fig_path = f"/home/emmanuel/projects/2020_rbig_rs/reports/figures/climate/"

Results¶

Mean Sea Level Pressure¶

CMIP5 vs ERA5 vs NCEP¶

variables = [
    'mslp_era_cmip', 
    'mslp_ncep_cmip'
]

filenames = [f"{results_path}{variable}.csv" for variable in variables]

results = ResultsLoader(filenames).load_dataframes()

# initializer plotter
plotter = PlotResults(results)

plotter.results = plotter.results[plotter.results['year'] < 2019]

Entropy¶

ent1_fig, ent1_ax = plotter.plot_entropy()

Total Correlation¶

tc1_fig, tc1_ax = plotter.plot_total_correlation()

Mutual Information¶

This is the MI between CMIP5 and the two models (ERA5 and NCEP)

mi1_fig, mi1_ax = plotter.plot_mutual_information(('model', ['cmip5']))

NCEP vs ERA5¶

Mean Sea Level Pressure¶

variables = [
    'mslp_ncep_era'
]

filenames = [f"{results_path}{variable}.csv" for variable in variables]

results = ResultsLoader(filenames).load_dataframes()

# initializer plotter
plotter = PlotResults(results)

plotter.results = plotter.results[plotter.results['year'] < 2017]

Entropy¶

plotter.plot_entropy();

Total Correlation¶

plotter.plot_total_correlation();

Mutual Information¶

The MI between ERA5 and the NCAR_NCEP_DOE_2 model.

plotter.plot_mutual_information(omit_models=('model', ['ncar_ncep_doe_2']));

Surface Pressure¶

variables = [
    'sp_ncep_era'
]

filenames = [f"{results_path}{variable}.csv" for variable in variables]

results = ResultsLoader(filenames).load_dataframes()

# initializer plotter
plotter = PlotResults(results)

plotter.results = plotter.results[plotter.results['year'] < 2018]

Entropy¶

plotter.plot_entropy();

Total Correlation¶

plotter.plot_total_correlation();

Mutual Information¶

The MI between ERA5 and NCEP.

plotter.plot_mutual_information(omit_models=('model', ['ncar_ncep_doe_2']));