• 数据来源：

NOAA Optimum Interpolation (OI) SST V2

• 下载地址：

https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html

• 空间分辨率:

5.0 degree latitude by 5.0 degree longitude global grid (72x36)87.5N,

• 覆盖范围

01/1856 to 2023/01

87.5N, 2.5E - 357.5E.

Monthly

Sea Level

Missing data is flagged with a value of -9.96921e+36.

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以下过程，数据选取时间为1983-2022年，为了时间维度的大小可以被12整除

导入库

import numpy as np
import matplotlib.pyplot as plt
import xarray as xr

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap,shiftgrid,addcyclic, cm
from netCDF4 import Dataset as netcdf # netcdf4-python module

from matplotlib.pylab import rcParams

读取 SST 数据

• 原本纬度排序是：90°N ~ -90°S，将其进行反正变为-90°S~90°N

data = xr.open_dataset(r'G:\code_daily\sst.mnmean.nc').sst.sel(time=slice('1983','2022'))

data
sst   = data[:,:,1:-1]
lons  = sst.lon
lats  = sst.lat[::-1]
sst

计算月平均气候态

• 将数据从 timexlatxlon 转化为 month x year x lat x lon
• 对转换后的year维度求平均得到月平均的结果

lons,lats = np.meshgrid(lons, lats) 
ntime,nrow,ncol = sst.shape
sstym  = sst.data.reshape((12,int(ntime/12), nrow,ncol),order='F') # convert (yearmonths|lat|lont) to (years|12months|lat|lon)
sstclm = sstym.mean(axis=1) # along the year axis
                 
sstclm.shape

(12, 180, 360)

绘图

fig, axes = plt.subplots(3,4,figsize=(18,12),dpi=200)
flat_axes = axes.reshape(-1)
m = Basemap(resolution='c', projection='ortho', lat_0=0., lon_0=180.)
# coordinates transformation
x, y = m(lons, lats)
# string month names
month_lst = ['January', 'February', 'March', 'April', 'May', 'June', 'July',
              'August', 'September', 'October', 'November', 'December']
imon = -1
for ax in flat_axes:
    m.ax = ax
    imon = imon + 1 
    cssst = m.contourf(x,y,sstclm[imon], 20, vmin=0, cmap='jet', vmax=30, extend='both') 
    m.colorbar(cssst,"right", size="5%", pad='2%') 
    plt.title( month_lst[imon], fontsize=20) 
    m.fillcontinents(color='gray', lake_color='aqua')

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