To calculate the contribution of carbon dioxide (CO2) to the current surface temperature of the Earth, we will need to use a combination of **physical principles** and **data on atmospheric concentrations of CO2** and other **greenhouse gases**. In this article we will try and understand the basics of calculating the carbon concentration affecting the surface temperature using Python programming. But, before let’s do a general outline of the steps we can follow:

- Determine the
**current atmospheric concentrations of CO2**and other greenhouse gases. We can find this information from various sources, including scientific papers, government agencies, and online databases. - Calculate the
**global mean surface temperature of the Earth**. This can be done by using temperature data from a large number of locations around the globe and averaging them. - Determine the
**amount of energy being absorbed by the Earth’s atmosphere from the sun**. This can be calculated using the solar constant, which is the amount of solar energy received by the Earth per unit area per unit time, and the Earth’s albedo, which is the fraction of solar energy reflected by the Earth’s surface and atmosphere. - Calculate the
**amount of energy being emitted by the Earth back into space**. This can be done using the Stefan-Boltzmann law, which states that the rate at which a blackbody (such as the Earth) emits energy is proportional to the fourth power of its temperature. - Calculate the
**difference between the energy absorbed by the Earth and the energy emitted back into space**. This will give us the net energy balance of the Earth, which is the excess energy that is trapped in the Earth’s atmosphere. - Determine the
**contribution of CO2 and other greenhouse gases to the net energy balance**. This can be done by**using the absorption and emission spectra of these gases**, which describe how they absorb and emit energy at different wavelengths. We can then calculate the amount of energy absorbed and emitted by each gas and add them up to determine the total contribution of all the gases. - Calculate the
**warming effect of the gases by comparing the net energy balance with and without the contribution of the gases**. The difference between the two will give us the warming effect of the gases.

This is a simplified version of the process that scientists use to calculate the warming effect of greenhouse gases. In practice, the calculations are more complex and may involve using advanced computer models and data from a wide range of sources.

There are several ways to determine the current atmospheric concentrations of CO2 and other greenhouse gases. One way is to use **data from atmospheric monitoring stations**, which measure the concentration of gases in the atmosphere at a specific location. These stations are typically operated by government agencies or research institutions, and their data is often made available to the public.

## Python code to retrieve and analyze data on atmospheric carbon concentrations from NOAA which is key for surface temperature impact study

Here’s an example of how we can use Python to retrieve and analyze data on atmospheric CO2 concentrations from the **National Oceanic and Atmospheric Administration (NOAA)**:

```
import requests
import pandas as pd
# Retrieve data on atmospheric CO2 concentrations from NOAA
url = "https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html"
html = requests.get(url).text
# Parse data using pandas
df = pd.read_html(html, header=0)[0]
# Print the first few rows of the data
print(df.head())
# Calculate the mean atmospheric CO2 concentration
mean_concentration = df["Trend"].mean()
print(f"The mean atmospheric CO2 concentration is {mean_concentration:.2f} ppm")
```

This will retrieve data on atmospheric CO2 concentrations from the NOAA website and print the first few rows of the data, as well as the mean atmospheric CO2 concentration.

We can also use other sources of data on atmospheric concentrations of greenhouse gases, such as the **World Meteorological Organization** (WMO) or the **European Monitoring and Evaluation Programme** (EMEP). There are also a number of online tools and APIs that provide access to atmospheric concentration data, such as the **Earth System Data Explorer** (ESDE) and the **Global Carbon Project** (GCP).

Keep in mind that atmospheric concentrations of greenhouse gases can vary by location, so it’s important to consider the specific location for which we want to determine the concentrations.

## Python code to calculate the global mean surface temperature of the Earth

To calculate the global mean surface temperature of the Earth, we can follow these steps:

**Obtain temperature data from a large number of locations**around the globe. This data can be in the form of daily or monthly average temperatures, and can be obtained from various sources such as government agencies, scientific papers, and online databases.**Convert the temperature data to a common unit**, such as degrees Celsius.**Average the temperature data**to obtain the global mean surface temperature. We can do this by summing the temperature data from all the locations and dividing by the number of locations.

Here is an example of how we can implement this in Python code:

```
# Assume that temperature_data is a list of temperature readings in degrees Celsius
# Calculate the sum of the temperature data
temperature_sum = sum(temperature_data)
# Calculate the number of temperature readings
num_temperatures = len(temperature_data)
# Calculate the global mean surface temperature
global_mean_surface_temperature = temperature_sum / num_temperatures
# Print the result
print(f"The global mean surface temperature is {global_mean_surface_temperature:.2f} degrees Celsius.")
```

This is a simplified version of the process used to calculate the global mean surface temperature. In practice, the calculations may be more complex and may involve accounting for factors such as the altitude and latitude of the locations, the time of year, and the influence of the oceans.

## Python code to **calculate the amount of energy being absorbed by the Earth’s atmosphere from the sun**

To calculate the amount of energy being absorbed by the Earth’s atmosphere from the sun, we can use the **solar constant and the Earth’s albedo**. The solar constant is **the amount of solar energy received by the Earth per unit area per unit time**, and is approximately 1361 Watts per square meter (W/m^2). The Earth’s albedo is the fraction of solar energy reflected by the Earth’s surface and atmosphere, and ranges from about 0.3 for the darkest surfaces to about 0.9 for the lightest surfaces.

Here is an example of how we can implement this calculation in Python:

```
import math
# Constants
SOLAR_CONSTANT = 1361 # W/m^2
# Inputs
earth_albedo = 0.3 # Assume that the Earth's albedo is 0.3
# Calculate the energy absorbed by the Earth's atmosphere
energy_absorbed = (1 - earth_albedo) * SOLAR_CONSTANT
# Print the result
print(f"The energy absorbed by the Earth's atmosphere is {energy_absorbed:.2f} W/m^2.")
```

This is a simplified version of the calculation, as the actual amount of energy absorbed by the Earth’s atmosphere will depend on a number of factors such as the angle of incidence of the solar radiation, the atmospheric conditions, and the surface reflectivity. In practice, we may need to use more complex models and data to accurately calculate the energy absorbed by the Earth’s atmosphere.

## Python code to **calculate the amount of energy being emitted by the Earth back into space**

To calculate the amount of energy being emitted by the Earth back into space, we can use the **Stefan-Boltzmann law**, which states that the **power emitted per unit surface area of a black body **(a theoretical object that absorbs all incident radiation)** is directly proportional to the fourth power of its temperature**. The constant of proportionality is the Stefan-Boltzmann constant, which is approximately 5.67 x 10^-8 W/m^2/K^4.

So, one of the important step for calculating the carbon contribution on surface temperature, here’s an example of how we can use the Stefan-Boltzmann law to calculate the amount of energy being emitted by the Earth back into space using Python code:

```
import math
# Stefan-Boltzmann constant
sigma = 5.67e-8
# Average temperature of the Earth in Kelvin
T = 288
# Surface area of the Earth in square meters
A = 4 * math.pi * 6.371e6**2
# Calculate the amount of energy being emitted by the Earth back into space
E = sigma * A * T**4
print(f"The amount of energy being emitted by the Earth back into space is {E:.2f} W")
```

This will output the amount of energy being emitted by the Earth back into space in watts.

## Python code to calculate the difference between the energy absorbed by the Earth and the energy emitted back into space

To calculate the difference between the energy absorbed by the Earth and the energy emitted back into space, we can use the concept of the net energy balance, which is the difference between the energy absorbed by the Earth and the energy emitted back into space.

One way to calculate the net energy balance is to use the following formula:

Net energy balance = Incoming solar radiation - Outgoing longwave radiation

Where:

- Incoming solar radiation is the
**amount of energy being emitted by the Sun that reaches the Earth’s surface**. - Outgoing longwave radiation is the
**amount of energy being emitted by the Earth back into space**.

Here’s an example of how we can use this formula to calculate the net energy balance in Python:

```
import math
# Stefan-Boltzmann constant
sigma = 5.67e-8
# Average temperature of the Earth in Kelvin
T = 288
# Surface area of the Earth in square meters
A = 4 * math.pi * 6.371e6**2
# Outgoing solar radiation in W/m^2
outgoing_solar_radiation = 1361
# Calculate outgoing longwave radiation in W/m^2
outgoing_longwave_radiation = sigma * A * T**4
# Calculate the net energy balance in W/m^2
net_energy_balance = outgoing_solar_radiation - outgoing_longwave_radiation
print(f"The net energy balance is {net_energy_balance:.2f} W/m^2")
```

This will output the net energy balance in watts per square meter. A positive value indicates that the Earth is absorbing more energy than it is emitting, while a negative value indicates that the Earth is emitting more energy than it is absorbing.

## Python code to determine the contribution of carbon and other greenhouse gases to the net energy balance for assessing the impact of surface temperature

To determine the contribution of CO2 and other greenhouse gases to the net energy balance, we can use the concept of **radiative forcing**, which is a measure of the amount of energy being absorbed or reflected by the Earth’s atmosphere. A positive radiative forcing indicates that the atmosphere is absorbing more energy than it is reflecting, while a negative radiative forcing indicates that the atmosphere is reflecting more energy than it is absorbing.

One way to calculate the radiative forcing due to CO2 and other greenhouse gases is to use the formula:

Radiative forcing = (Outgoing solar radiation - Outgoing longwave radiation) * (1 - Transmissivity)

Where:

- Outgoing solar radiation is the
**amount of energy being emitted by the Sun that reaches the Earth’s surface**. - Outgoing longwave radiation is the
**amount of energy being emitted by the Earth back into space**. - Transmissivity is the
**fraction of solar radiation that is transmitted through the Earth’s atmosphere**.

So, one of the important step for calculating the carbon contribution on surface temperature, we can use the below code to calculate the contribution of carbon (CO2) and other greenhouse gases to the net energy balance:

```
import math
# Stefan-Boltzmann constant
sigma = 5.67e-8
# Average temperature of the Earth in Kelvin
T = 288
# Surface area of the Earth in square meters
A = 4 * math.pi * 6.371e6**2
# Outgoing solar radiation in W/m^2
outgoing_solar_radiation = 1361
# Calculate outgoing longwave radiation in W/m^2
outgoing_longwave_radiation = sigma * A * T**4
# Calculate transmissivity
transmissivity = outgoing_longwave_radiation / outgoing_solar_radiation
# Calculate radiative forcing in W/m^2
radiative_forcing = (outgoing_solar_radiation - outgoing_longwave_radiation) * (1 - transmissivity)
print(f"The contribution of CO2 and other greenhouse gases to the net energy balance is {radiative_forcing:.2f} W/m^2")
```

This will output the radiative forcing due to CO2 and other greenhouse gases in watts per square meter. A positive value indicates that the atmosphere is absorbing more energy than it is reflecting, while a negative value indicates that the atmosphere is reflecting more energy than it is absorbing.

## Python code to **calculate the warming effect of greenhouse gases, by comparing the net energy balance with and without the contribution of the gases**

To calculate the warming effect of greenhouse gases, we can compare the net energy balance with and without the contribution of the gases**. **We can do this by calculating the **radiative forcing due to the gases**, as described in the previous python code, and comparing it to the radiative forcing in the absence of the gases.

So, one of the important step for calculating the carbon contribution on surface temperature, here’s an example of how we can calculate the warming effect of greenhouse gases using Python code:

```
import math
# Stefan-Boltzmann constant
sigma = 5.67e-8
# Average temperature of the Earth in Kelvin
T = 288
# Surface area of the Earth in square meters
A = 4 * math.pi * 6.371e6**2
# Outgoing solar radiation in W/m^2
outgoing_solar_radiation = 1361
# Calculate outgoing longwave radiation in W/m^2
outgoing_longwave_radiation = sigma * A * T**4
# Calculate transmissivity
transmissivity = outgoing_longwave_radiation / outgoing_solar_radiation
# Calculate radiative forcing due to greenhouse gases in W/m^2
radiative_forcing_gases = (outgoing_solar_radiation - outgoing_longwave_radiation) * (1 - transmissivity)
# Calculate radiative forcing in the absence of greenhouse gases in W/m^2
radiative_forcing_no_gases = outgoing_solar_radiation - outgoing_longwave_radiation
# Calculate the warming effect of the gases in W/m^2
warming_effect = radiative_forcing_gases - radiative_forcing_no_gases
print(f"The warming effect of the gases is {warming_effect:.2f} W/m^2")
```

This will output the warming effect of the gases in watts per square meter. A positive value indicates a warming effect, while a negative value indicates a cooling effect.

## Closing Note: Calculating the amount of warming CO2 contributes towards the current surface temperature of our planet

It is important to calculate the amount of warming that CO2 contributes towards the current surface temperature of the Earth because this information can help us understand the extent to which human activities are contributing to global warming and the potential impacts of this warming on the environment and human society. By understanding the role of CO2 in global warming, we can develop strategies to reduce CO2 emissions and mitigate the negative impacts of global warming.

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