Here is the method to calculate CI @95% :

How to Calculate Confidence Intervals in Python? - GeeksforGeeks

Good luck.

Follow these steps:

1. Import the required libraries:

```python

import numpy as np

import matplotlib.pyplot as plt

import netCDF4 as nc

```

2. Read the netCDF file and extract the data:

```python

# Read the netCDF file

data = nc.Dataset('path_to_your_file.nc')

# Extract the variable of interest (assuming it's called 'variable_name')

variable = data.variables['variable_name'][:]

```

3. Perform the statistical calculations to find the 95% confidence level:

```python

# Calculate the mean and standard deviation

mean_value = np.mean(variable)

std_dev = np.std(variable)

# Calculate the confidence interval

confidence_interval = 1.96 * std_dev / np.sqrt(len(variable))

# Calculate the upper and lower bounds of the confidence interval

lower_bound = mean_value - confidence_interval

upper_bound = mean_value + confidence_interval

```

4. Plot the spatial data and overlay the confidence interval:

```python

# Assuming you have latitude and longitude arrays

lat = data.variables['latitude'][:]

lon = data.variables['longitude'][:]

# Create a meshgrid for the latitude and longitude

lon_mesh, lat_mesh = np.meshgrid(lon, lat)

# Plot the spatial data

plt.contourf(lon_mesh, lat_mesh, variable)

# Overlay the confidence interval

plt.contour(lon_mesh, lat_mesh, variable, levels=[lower_bound, upper_bound], colors='red')

# Add colorbar and labels

plt.colorbar()

plt.xlabel('Longitude')

plt.ylabel('Latitude')

# Show the plot

plt.show()

```

Replace 'path_to_your_file.nc' with the actual path to your netCDF file, and 'variable_name' with the name of the variable you want to analyze. Adjust the figure settings, color scheme, and labels according to your preferences.

Hope it helps

Mohammad Imam Thanks for the solution.