Data source : https://ads.nipr.ac.jp/vishop/#/extent
REGION SELECTOR = Antarctic
At the bottom of the page : Download the sea ice extent (CSV file) - seasonal dataset

From the National Institute of Polar Research (Japan) website:

The sea-ice extent is calculated as the areal sum of sea ice covering the ocean where sea-ice concentration (SIC) exceeds a threshold (15% for AMSR-E). SICs are derived from various satellite-borne passive microwave radiometer (PMR) sensors using the algorithm developed and provided by Dr. Comiso of NASA GSFC through a cooperative relationship between NASA and JAXA. The following sensor’s data were used;
• Jan. 1980 ～ Jul. 1987 ： SMMR
• Jul. 1987 ～ Jun. 2002 ： SSM/I
• Jun. 2002 ～ Oct. 2011 ： AMSR-E
• Oct. 2011 ～ Jul. 2012 ： WindSat
• Jul. 2012 ～ the present ： AMSR2

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.style.use("fivethirtyeight")

CSV_FILE_PATH = "./VISHOP_EXTENT_GRAPH_Antarctic.csv"
FS = (12, 7)  # figure size

Load the Data

df = pd.read_csv(CSV_FILE_PATH)
df.head(3)
#num month date ... 2021 2022 2023
0 0 1 1 ... 6568300.0 5778923.0 4777328.0
1 1 1 2 ... 6451907.0 5570948.0 4644691.0
2 2 1 3 ... 6325097.0 5411740.0 4485865.0

3 rows × 54 columns

We remove columns that are not specific years (#num, month, date, time[second], 1980's Average, …)

cols = df.columns
cols = [c for c in cols if (len(c) == 4) and c.isnumeric() and (c.startswith("19") or c.startswith("20"))]
df = df[cols]
df = df.astype(float)
df = df.replace(-9999.0, np.nan)
df.head(3)
1978 1979 1980 ... 2021 2022 2023
0 NaN NaN 5966499.0 ... 6568300.0 5778923.0 4777328.0
1 NaN 6988174.0 NaN ... 6451907.0 5570948.0 4644691.0
2 NaN NaN 5855460.0 ... 6325097.0 5411740.0 4485865.0

3 rows × 46 columns

Non-leap years

The dataframe columns correspond to years and rows to (month , day) combinations. Thus, February 29th has missing values on non-leap years. We shift the values on these years in order to have a day-of-year row index without missing values on the 29th of February:

df.iloc[58:61][[str(y) for y in range(2014, 2024)]]
2014 2015 2016 ... 2021 2022 2023
58 3753923.0 3800984.0 2857127.0 ... 3197871.0 2211479.0 2063912.0
59 NaN NaN 2853039.0 ... NaN NaN NaN
60 3772864.0 3807337.0 2863943.0 ... 3336462.0 2231068.0 2095439.0 
for year in range(1978, 2024):
    if (year - 1972) % 4 != 0:
        year_str = str(year)
        if year_str in df:
            df.loc[59:365, year_str] = df.loc[59:365, year_str].shift(-1)

df.iloc[58:61][[str(y) for y in range(2014, 2024)]]
2014 2015 2016 ... 2021 2022 2023
58 3753923.0 3800984.0 2857127.0 ... 3197871.0 2211479.0 2063912.0
59 3772864.0 3807337.0 2853039.0 ... 3336462.0 2231068.0 2095439.0
60 3805451.0 3803617.0 2863943.0 ... 3474391.0 2263092.0 2095754.0

Long time range daily mean

year_start = 1978
year_end = 2012
current_year = 2023
df[f"{year_start}-{year_end} mean"] = df[
    [str(y) for y in range(year_start, year_end + 1)]
].mean(axis=1)

Figure

alpha_1 = 0.2
color_1 = "silver"
alpha_2 = 0.4
color_2 = "grey"
color_3 = "black"

ax = (df[f"{year_start}"] - df[f"{year_start}-{year_end} mean"]).plot(
    figsize=FS, color=color_1, alpha=alpha_1
)
for year in [str(y) for y in range(year_start + 1, year_end)]:
    ax = (df[year] - df[f"{year_start}-{year_end} mean"]).plot(
        ax=ax, color=color_1, alpha=alpha_1
    )
ax = (df[f"{year_end}"] - df[f"{year_start}-{year_end} mean"]).plot(
    ax=ax, color=color_1, label=f"{year_start}-{year_end}", alpha=alpha_1
)

ax = (df[f"{year_end+1}"] - df[f"{year_start}-{year_end} mean"]).plot(
    ax=ax, color=color_2, alpha=alpha_2
)
for year in [str(y) for y in range(year_end + 2, current_year - 1)]:
    ax = (df[year] - df[f"{year_start}-{year_end} mean"]).plot(
        ax=ax, color=color_2, alpha=alpha_2
    )
ax = (df[f"{current_year-1}"] - df[f"{year_start}-{year_end} mean"]).plot(
    ax=ax, color=color_2, label=f"{year_end+1}-{current_year-1}", alpha=alpha_2
)

ax = (df[f"{current_year}"] - df[f"{year_start}-{year_end} mean"]).plot(
    ax=ax, color=color_3, label=f"{current_year}", alpha=1.0
)

plt.hlines(y = 0, xmin = 0, xmax = 365, alpha=0.7, linewidth=0.5)
_ = ax.set_ylim(-3e6, +3e6)
_ = ax.legend()
_ = ax.set(
    title=f"Antarctic sea ice extent anomaly \n w.r.t. {year_start}-{year_end} mean",
    xlabel="Day of year",
    ylabel="Sea ice extent anomaly (million $km^2$)",
)

output_13_0