Description

import netCDF4 import xarray as xr import pandas as pd
from matplotlib import pyplot as plt
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1. Global methane levels from 2002
Atmospheric methane abundance is indirectly observed by various satellite instruments. These instruments measure spectrally resolved near-infrared and infrared radiation reflected or emitted by the Earth and its atmosphere. In the measured signal, molecular absorption signatures from methane and constituent gasses can be identified. It is through analysis of those absorption lines in these radiance observations that the averaged methane abundance in the sampled atmospheric column can be determined.
In [ ]: # Open a netCDF4 file ds1 = xr.open_dataset(“200301_202006-C3S-L3_GHG-PRODUCTS-OBS4MIPS-MERGED-v4.3.nc”, eng ds1
Out[ ]: xarray.Dataset

►Dimensions:
▼Coordinates: (time: 210, bnds: 2, lat: 36, lon: 72, pressure: 10)
time (time) datetime64[ns] 2003-01-16T12:00:0… lat (lat) float64 -87.5 -82.5 -77.5 … … lon (lon) float64 -177.5 -172.5 … 172.…
▼Data variables:
time_bnds (time, bnds) datetime64[ns] … lat_bnds (lat, bnds) float64 … lon_bnds (lon, bnds) float64 … pre (pressure) float64 … pre_bnds (pressure, bnds) float64 … land_fraction (lat, lon) float64 … xch4 (time, lat, lon) float32 … xch4 nobs (time lat lon) float64 xch4_nobs (time, lat, lon) float64 … xch4_stderr (time, lat, lon) float32 … xch4_stddev (time, lat, lon) float32 … column_averagin… (time, pressure, lat, lon) float32 …
vmr_profile_ch4_… (time, pressure, lat, lon) float32 …
►Attributes: (28)
1.1 [5 points] Compute methane climatology for each month, and plot your results in 12 panels.
In [ ]: ds1_clim =ds1.xch4.groupby(‘time.month’).mean() ds1_clim.plot(col=”month”, col_wrap=4, robust=True) plt.title(‘methane climatology for each month’) plt.show()

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ds1.xch4.sel(lon =-150, lat =-15, method =’nearest’).groupby(‘time.year’).mean(dim =
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[<matplotlib.lines.Line2D at 0x1ae0cc64640>] Out[ ]:

2. Niño 3.4 index
In this problem set, you will use the sea surface temperature (SST) data from NOAA. Download the netCDF4 file (NOAA_NCDC_ERSST_v3b_SST.nc) here.
ds2 =xr.open_dataset(‘NOAA_NCDC_ERSST_v3b_SST.nc’, engine=”netcdf4″) ds2.info
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<bound method Dataset.info of <xarray.Dataset> Out[ ]:
Dimensions: (lat: 89, lon: 180, time: 684) Coordinates:
* lat (lat) float32 -88.0 -86.0 -84.0 -82.0 -80.0 … 82.0 84.0 86.0 88.0
sst (time, lat, lon) float32 …
Attributes:
Conventions: IRIDL source: https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/.ERSST/… history: extracted and cleaned by Ryan Abernathey for Research Compu…>
1.1 [10 points] Compute monthly climatology for SST from Niño 3.4 region, and subtract climatology from SST time series to obtain anomalies.
nino_region =ds2.sst.sel(lat=slice(-5, 5),lon =slice(190,240)) nino_month =nino_region.groupby(‘time.month’) nino_month.mean().plot(col=”month”, col_wrap=4, robust=True) plt.title(‘monthly climatology for SST from Nino 3.4 region’) plt.show()
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1.2 [10 points] Visualize the computed Niño 3.4. Your plot should look similar to this one.

# time_plot =nino_region_anom.time
# print(nino_plot)
# nino =pd.DataFrame(nino_plot.where(nino_plot >0))
# nina =pd.DataFrame(nino_plot.where(nino_plot <0)) –
]
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plt.grid(linestyle =’–‘)
plt.fill_between(nino_plot.index,0,nino_plot.sst, where= nino_plot.sst>0,facecolor =
plt.fill_between(nino_plot.index,0,nino_plot.sst, where=nino_plot.sst<0, facecolor =
plt.plot(nino_plot.index, nino_plot.sst, color =’black’, linewidth =0.5, label =’3mt plt.ylabel(‘Anomaly in Degrees C’) plt.xlabel(‘Year’) plt.title(‘SST Anomaly in Nino 3.4 Region (5N-5S,120-170W)’) plt.legend() plt.show() h

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3. Explore a netCDF dataset
Browse the NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC) website. Search and download a dataset you are interested in. You are also welcome to use data from your group in this problem set. But the dataset should be in netCDF format, and have temporal information.
3.1 [5 points] Plot a time series of a certain variable with monthly seasonal cycle removed.
ds3 = xr.open_dataset(‘GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc’)
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[<matplotlib.lines.Line2D at 0x1ae0ebb3730>] Out[ ]:

In [ ]: # Plot a time series of a certain variable with monthly seasonal cycle removed by mean ds3.lwe_thickness.groupby(‘time.year’).mean(dim =(‘time’,’lat’,’lon’)).plot()
[<matplotlib.lines.Line2D at 0x1ae0edffee0>] Out[ ]:

3.2 [10 points] Make at least 5 different plots using the dataset.
In [ ]: # Plot mean DEM in the latest month ds3.lwe_thickness[-1].plot()
<matplotlib.collections.QuadMesh at 0x1ae14552220> Out[ ]:

In [ ]: # mean time series ds3.lwe_thickness.mean(dim=(‘lon’, ‘lat’)).plot()
[<matplotlib.lines.Line2D at 0x1ae1a06e430>] Out[ ]:

In [ ]: import numpy as np
# Create the weights weights = np.cos(np.deg2rad(ds3.lat)) weights.dims lwe_weighted = ds3.lwe_thickness.weighted(weights) lwe_weighted.mean(dim=(‘lon’, ‘lat’)).plot() plt.title(“Corrected global mean DEM”)
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In [ ]: # Plot the averaged global SST at Sep. ds3.lwe_thickness.groupby(‘time.month’).mean()[8,:,:].plot(vmin=-5, vmax=35)
<matplotlib.collections.QuadMesh at 0x1ae146156d0> Out[ ]:

In [ ]: # Plot zonal mean
ds3.lwe_thickness.mean(dim=’lon’).plot.contourf(x=’time’, levels=12, vmin=-5, vmax=35)
<matplotlib.contour.QuadContourSet at 0x1ae10a86f40> Out[ ]:

# Plot climatology at a specific point
ds3.lwe_thickness.sel(lon=294.55, lat=30.5, method=’nearest’).plot()
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[<matplotlib.lines.Line2D at 0x1ae160babe0>] Out[ ]:

# Group data by month
group3 = ds3.lwe_thickness.groupby(‘time.month’)
# Apply mean to grouped data, and then compute the anomalies lwe_anom = group3 – group3.mean(dim=’time’)
# Use resample() function at a frequency of 3 years resample_obj = lwe_anom.resample(time=”3Y”)

# Show the resample object resample_obj
# Apply mean() function to the resample object and get results ds_anom_resample = resample_obj.mean(dim=”time”) ds_anom_resample

# Plot anomalies
lwe_anom.sel(lon=294.55, lat=30.5,
method=’nearest’).plot(label =’anom’)
# Plot 3-year resampled anomalies
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ds_anom_resample.sel(lon=114.55+180, lat=22.5, method=’nearest’).plot(label =’3-year resampled’)

plt.legend() plt.show()