导入需要用到的模块

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

生成模拟数据

n_years = 10 # 总共模拟10年的数据 n_months = 12 # 每年12个月 eu_index = np.random.normal(0, 1, size=(n_years, n_months)) # 生成欧亚遥相关指数数据 circulation_field = np.random.normal(0, 1, size=(n_years, n_months)) # 生成环流场数据 temperature = np.random.normal(0, 1, size=(n_years, n_months)) # 生成我国气温数据 years = np.arange(1, n_years+1) # 年份序列

计算欧亚遥相关指数年际变化的时间序列

eu_index_annual_mean = np.mean(eu_index, axis=1) # 每年的平均值 eu_index_annual_change = (eu_index_annual_mean - np.mean(eu_index_annual_mean)) / np.std(eu_index_annual_mean) # 年际变化 eu_index_time_series = pd.Series(eu_index_annual_change, index=years, name='EU Index')

绘制欧亚遥相关指数年际变化的时间序列

plt.figure() sns.lineplot(data=eu_index_time_series) plt.xlabel('Year') plt.ylabel('EU Index') plt.title('EU Index Annual Change')

计算欧亚遥相关指数与同期环流场的相关系数

corr_circulation = np.corrcoef(eu_index.flatten(), circulation_field.flatten())[0,1] print(f'Correlation between EU Index and Circulation Field: {corr_circulation:.2f}')

计算欧亚遥相关指数与同期我国气温的相关系数

corr_temperature = np.corrcoef(eu_index.flatten(), temperature.flatten())[0,1] print(f'Correlation between EU Index and Temperature: {corr_temperature:.2f}')