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Compute correlation maps nino3-Sea Level Pressure

Correlation analysis are often used to examine climatological systems such as teleconnections. In this notebook, we will calculate the corelationship between ssta anomalies in nino3 area and mean sea level pressure(MSLP).

SLP Data Source:

Brief Description:

NCEP/NCAR Reanalysis 1
Temporal Coverage:

4-times daily, daily and monthly values for 1948/01/01 to present
Spatial Coverage:

2.5 degree latitude x 2.5 degree longitude global grid (144x73)
90N - 90S, 0E - 357.5E

1. Load basic libs



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% matplotlib inline

import numpy as np
from scipy.stats import pearsonr

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

from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 9

2. Read nino3 SSTA series

Please keep in mind that the nino3 SSTA series lies between 1970 and 1999
Recall ex2



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npzfile = np.load('data/ssta.nino3.30y.npz')
npzfile.files









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['ssta_series']



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ssta_series = npzfile['ssta_series']
ssta_series.shape









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(360L, 1L)

3. Read monthly mean sea levep pressure



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ncset= netcdf(r'data/slp.mon.mean.1970.1999.nc')

lons = ncset['lon'][:]  
lats = ncset['lat'][:]            
slp = ncset['slp'][:,:,:]   

nt,nlat,nlon = slp.shape
ngrd = nlat*nlon
nyr  = nt/12

4. Remove SLP seasonal cycle



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slp_grd  = slp.reshape((12,nyr, ngrd), order='F').transpose((1,0,2))
slp_clim = np.mean(slp_grd, axis=0)
slp_anom = (slp_grd - slp_clim).transpose((1,0,2)).reshape((nt, ngrd), order='F')

slp_anom[:,1].shape









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(360L,)

5. Calculate correlationship between ssta and slp



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corr = np.zeros((1, ngrd))
pval = np.zeros((1, ngrd))



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for igrd in np.arange(ngrd):
    crr,pvalue = pearsonr(ssta_series[:,0], slp_anom[:,igrd])
    corr[0,igrd] = crr
    pval[0,igrd] = pvalue



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corr = corr.reshape((nlat,nlon), order='F')
pval = pval.reshape((nlat,nlon), order='F')

6. Visualize

Correlation and correlation at 5% significant level



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# Correlation
m = Basemap(projection='cyl', llcrnrlon=min(lons), llcrnrlat=min(lats),
        urcrnrlon=max(lons), urcrnrlat=max(lats))

x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1.0, 1.0, 21)

fig = plt.figure(figsize=(15,12))
ax = fig.add_subplot(211)
cs = m.contourf(x, y, corr.squeeze(), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()

cb = m.colorbar(cs)
cb.set_label('Correlation', fontsize=12)
ax.set_title('HCM Nino3-SLP', fontsize=16)

#correlation at 5% significant level
ax = fig.add_subplot(212)
corr_sig = np.ma.masked_array(corr, mask=(pval>=0.05))
cs = m.contourf(x, y, corr_sig.squeeze(), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()

cb = m.colorbar(cs)
cb.set_label('Correlation', fontsize=12)
ax.set_title('HCM Nino3-SLP (sign at 0.05)', fontsize=16)









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<matplotlib.text.Text at 0x10b91ba8>

References

http://unidata.github.io/netcdf4-python/

John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), DOI:10.1109/MCSE.2007.55

Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37

Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.



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