# Assign the monthly gridded DOT anomaly to the array
dot_anom_ts[:, :, t] = nc.variables[
'dynamic_ocean_topography_anomaly'][:]
nc.close()
# Move the month index along by 1
t += 1
## Cycle through each grid cell and calculate the trend of the data
dot_anom_regress = np.full((59, 361), fill_value=np.NaN)
for ilat in range(len(lat)):
for ilon in range(len(lon)):
# If there are nans in the data
if np.any(np.isfinite(dot_anom_ts[ilat, ilon, :])):
regress = stats.linregress(
time, funct.inpaint_nans(dot_anom_ts[ilat, ilon, :]))
dot_anom_regress[ilat, ilon] = (regress[0] * 1000)
## Calculate the mean circumpolar trend
# Calculate the grid size for each cell
grid_lon, grid_lat = np.meshgrid(lon, lat)
# Calculate the surface area of each cell
S = funct.surface_area(grid_lat, grid_lon, 0.5, 1.0)
total_area = np.nansum(np.nansum(~np.isnan(dot_anom_regress) * S))
total_trend = np.nansum(dot_anom_regress * S) / total_area
print('The total trend is:', total_trend, 'mm / year')
nc = Dataset(altimetry_file, 'r')
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
dot_anom_ts[:, :, t] = nc.variables['dynamic_ocean_topography_anomaly'][:]
nc.close()
# Calculate the correlation between the time series and the altimetry data
dot_anom_xcorr = np.full((59, 361), fill_value=np.NaN)
dot_anom_xcorr_pvalues = np.full((59, 361), fill_value=np.NaN)
for ilat in range(len(lat)):
for ilon in range(len(lon)):
# If there are nans in the altimetry data
if np.any(np.isfinite(dot_anom_ts[ilat, ilon, :])):
xcorr = stats.spearmanr(funct.inpaint_nans(dot_anom_ts[ilat, ilon, :]), bpr)
dot_anom_xcorr[ilat, ilon] = xcorr[0]
dot_anom_xcorr_pvalues[ilat, ilon] = xcorr[1]
pl.figure()
pl.clf()
m = Basemap(projection='spstere', boundinglat=-50, lon_0=180, resolution='l')
m.drawmapboundary()
m.drawcoastlines(zorder=10)
m.fillcontinents(zorder=10)
m.drawparallels(np.arange(-80., 81., 20.), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180., 181., 20.), labels=[0, 0, 0, 1])
grid_lats, grid_lons = np.meshgrid(lat, lon)
stereo_x, stereo_y = m(grid_lons, grid_lats)
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
# Assign the monthly gridded DOT anomaly to the array
ssh_anom_ts[:, :, t] = nc.variables['sea_surface_height_anomaly'][:]
nc.close()
# Move the month index along by 1
t += 1
## Cycle through each grid cell and calculate the trend of the data
ssh_anom_regress = np.full((59, 361), fill_value=np.NaN)
for ilat in range(len(lat)):
for ilon in range(len(lon)):
# If there are nans in the data
if np.any(np.isfinite(ssh_anom_ts[ilat, ilon, :])):
regress = stats.linregress(time, funct.inpaint_nans(ssh_anom_ts[ilat, ilon, :]))
ssh_anom_regress[ilat, ilon] = (regress[0] * 1000)
## Calculate the mean circumpolar trend
# Calculate the grid size for each cell
grid_lon, grid_lat = np.meshgrid(lon, lat)
# Calculate the surface area of each cell
S = funct.surface_area(grid_lat, grid_lon, 0.5, 1.0)
total_area = np.nansum(np.nansum(~np.isnan(ssh_anom_regress) * S))
total_trend = np.nansum(ssh_anom_regress * S) / total_area
print('The total trend is:', total_trend, 'mm / year')
