Week 10 (W05 Feb08) Global Climate Dataset

1- Summary

2 - Dataset Stats

Global Climate Data (GCD) : Main Dataset

• Number of files: 100.791
• Format: .dly files (Complete Works Wordprocessing Template)
• Size: 26.5 GB
• Features: 46
• Source Date: 1763 - 2016
• Missing values: 43.9%

World Bank (WB) : Complementary Dataset

• Number of files: 1
• Format: .csv
• Size: ~15 MB
• Features: 82
• Source Date: 1960 - 2016
• Missing values: 49.2%

3 - Collective Analysis of our work

To see Climate Change (Temperature Rise) globally , we have plotted maps for average temperature (Celsius) for year 1960 and 2016 for all the Countries.

The Maps clearly show the Climate Change (Rise in Temperature)

For Example:

United States (1960) = 11.5 C

United States (2016) = 12.7 C

Russia (1960) = -6 C

Russia (2016) = -2.3 C

Canada (1960) = -4.4 C

Canada (2016) = -1.6 C

Here, individually each country shows different amount of Tavg increase like Russia shows 3.7 degrees, US shows 1.2 degrees, Canada shows 2.8 but globally it has been found out that overall for the entire world temperature rise is around 2 degrees per 100 years. Individual countries may show variation in temperature rise because of external factors affecting that region for example available area, closeness to the water bodies, exposure to pollution level, etc so all countries over the world will not show same temperature rise but globally for the whole world it has been found out that temperature rises around 2 degree in 100 years from available data of past years.

• Northern Hemisphere is warming faster due to heat transport from oceans in the south

Factors Contributing For Temperature Change:

• Greenhouse gases shows positive correlation with Temperature Rise

The Major Released Greenhouse Gasses Globally by percentage

* Major Released Gas is - Carbon Dioxide (CO2) = 51%  

Countries contributed abundantly for CO2 is shown below in map and pie chart:

* Major Contributors For CO2 are: China, United States, India, Germany, Russia, Brazil.  

Countries contributed abundantly for methane is shown below in map and pie chart

* Major Contributors For Methane are: China, United States, India, Russia. 

Countries contributed abundantly for Nitrous Oxide is shown below in Map and Pie Chart

* Major Contributors For Nitrous Oxide are: China, United States, India, Russia.  

Sources that affect CO2 emissions

• First we correlated all the available variables with CO2.The variables which have shown positive correlation
  are further grouped into specific variable for example Electricity production from coal and Electricity production from nuclear source are grouped into one variable called Electricity.

• The below mentioned features are major sources for the emission of Co2

Major sources for the emissions Of CO2 is shown in pie charts below

• China

  1. Electricity - 52%
  2. Manufacturing Industries and Construction- 32%

• India

  1. Electricity - 40%
  2. Agriculture - 23%

• Germany

  1. Energy - 40 %
  2. Industry,Agriculture - 32%

• United States

  1. Electricity - 50%
  2. Transportation - 29%

Reasons For the Use of Major Sources For CO2:

• China :

  1. 73 percent of Electricity in china is produced using Coal.
  2. China is a World Manufacturer for example China produces world's 80% of Air conditioners , 70% of mobile
     phones.

• India :

  1. India is Facing Massive Power Shortage, Around 30 percent of Population still don't have access.
  2. India has 4th largest reservoir of Coal and produces 66 percent of Electricity from Coal.

• Germany :

  1. In 2007, Germany was hugely dependent on Coal, Nuclear for Energy but it has declined the use coal and
     invested in renewable Energy but in 2011 Merkel's shuts down 7 nuclear reactor out of 17.

• United States :

  1. United States Energy sector is largely hold by private companies for example Koch Companies uses abundantly fossil fuels to run Business.
  2. Climate Change bill didn't pass in United States congress, Because 131 members of Congress denied Climate Change, From which 38 are senate members, who are also members of American For Prosperity which get highly
     funded by Koch Brothers.

Major Sources For The Emission Of Methane is shown in below Pie Charts

• China

  1. Coal - 31%
  2. Agriculture Farming - 26%

• India

  1. Coal - 39%
  2. Agriculture Farming - 32%

• Germany

  1. Agriculture Farming - 53 %
  2. Waste Water Treatment - 22%

• United States

  1. Natural Gas and Petroleum - 35%
  2. Agriculture Farming- 31%

Major Sources For The Emission Of Nitrous Oxide is shown in below Pie Charts

• India

  1. Transport - 69%

• Germany

  1. Transport - 53 %

• United States

  1. Agriculture Soil Management - 84%

4 - Assumptions

• We assume a worst-case scenario in our predictions
• For emissions we take into consideration the biggest contributors and a 20% of the global emissions
• Emissions affect locations uniformly (no distance factor taken into account)
• 50% of emissions travel in the atmosphere, rest is absorbed

5 - Prediction of emissions

In numbers for 2067:

• Carbon dioxide (kt): 760210493
• Methane (thousand metric tons of CO2 equiv.): 48888139
• Nitrous oxide (kt of CO2 equiv.): 34030479
• Greenhouse gas emissions (thousand metric tons of CO2 equiv.): 454203705

6 - Prediction of environmental variables

In order to predict various environmental variables over the years we tested the following two methods.

• Auto-Regression
• Auto-Regression Moving Average [Details-https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model]

The errors we got are shown below. We chose the Auto-Regression with moving average for our purpose due to the lower error.

We aggregated the variables from the following countries to estimate the global impact. (total countries included 115/196): Angola Albania United Arab Emirates Argentina Armenia Australia Austria Azerbaijan Belgium Benin Bangladesh Bulgaria Bahrain Bosnia and Herzegovina Belarus Bolivia Brazil Brunei Darussalam Botswana Canada Switzerland Chile China Cote d'Ivoire Cameroon Congo, Rep. Colombia Costa Rica Cuba Cyprus Czech Republic Germany Denmark Dominican Republic Algeria Ecuador Egypt, Arab Rep. Eritrea Spain Estonia Ethiopia Finland France Gabon United Kingdom Georgia Ghana Gibraltar Greece Guatemala Hong Kong SAR, China Honduras Croatia Haiti Hungary Indonesia India Ireland Iran, Islamic Rep. Iraq Iceland Israel Italy Jamaica Jordan Japan Kazakhstan Kenya Kyrgyz Republic Cambodia Korea, Rep. Kuwait Lebanon Libya Sri Lanka Lithuania Luxembourg Latvia Morocco Moldova Mexico Macedonia, FYR Malta Myanmar Mongolia Mozambique Malaysia Namibia Nigeria Nicaragua Netherlands Norway Nepal New Zealand Oman Pakistan Panama Peru Philippines Poland Korea, Dem. People’s Rep. Portugal Paraguay Qatar Romania Russian Federation Saudi Arabia Sudan Senegal Singapore El Salvador Slovak Republic Slovenia Sweden Syrian Arab Republic Togo Thailand Tajikistan Turkmenistan Trinidad and Tobago Tunisia Turkey Tanzania Ukraine Uruguay United States Uzbekistan Venezuela, RB Vietnam Yemen, Rep. South Africa Congo, Dem. Rep. Zambia Zimbabwe

We have tried to take these 115 countries instead of 196 because they are the major contributors to the impact of these variables on emissions over the years. What we see in these graphs is that variable such as Energy consumption (through non-renewable sources) is rising over the predicted years exponentially, which shows an alarming situation as it may proclaim heavy dependence on non-renewable sources and emissions of greenhouse gases.

We tried prediction similarly for other variables such as population, Forested area, Agriculture, and got similar results contributing to an increase in emissions in worst case scenario. Usually, big countries like China, USA (main emission contributor) showed an increase in population over the years except India which showed a bit decline. Other developed European countries and east Asian countries (Japan, South Korea) showed decline in the population over the years. For Agriculture too USA, Germany, India showed increase in Agriculture contribution compared to other countries.

About the forested area variable most countries showed increase in forestration except India and USA.

Our results are based on our model used and shows that some are physically correct, but some others show that our results do not match to the common expectation. The misinterpreted results is most likely due to our models mechanics. We notice the importance of trying to research and understand the findings of data analysis in order to conclude about the efficiency of our model.

7 - Prediction of temperature

• PLSR (Principal Least Square Regression Method) [Description: https://en.wikipedia.org/wiki/Partial_least_squares_regression]

• PCR (Principal component Regression) [Description: https://en.wikipedia.org/wiki/Principal_component_regression]

• Neural Network

X(t): Input Matrix > Emissions y(t): Target > Temperature Algorithm used for Training: Levenberg Marquardt Number of hidden neurons: 10 Number of Delays: 2

The errors are shown in the following table:

Our real data are from 1960 to 2016, while the predicted ones after 2017. In the following graphs we present the results for two case studies, Austria and USA. Average temperature rise in Austria

Average temperature rise in USA

So here we tried to show how the country like Austria with much less emissions can be the major target of temperature rise and also country like USA, a major contributor to emissions can itself be affected by it, emphasizing the global effect of climate warming. The average temperature rise of Austria calculated from the predictions (2017-2067) is 0.83 Celsius and average temperature rise in USA calculated from predictions (2017-2067) is 0.68. Based on our analysis in previous sections, we estimated an average warming of 2-3 degrees Celsius every 100 years. With our model results we receive a bit less than 1-degree rise in 50 years, which would translate into 1-2 degrees every 100 years. This number is smaller than our expectation but still manages to show a continuous increase of the temperature. Improvements in our neural network, as well as the prediction of emissions could be made to improve the accuracy of the model. Such improvements would be the employment of different more detailed neural network algorithms, that are more time-consuming though.

8 - Conclusions

• Temperature rise is not taking place locally, but globally. Even if specific countries do not emit high amounts of emissions their temperature is equally affected by global emissions. [case studies: both Austria and USA are affected]

• Northern Hemisphere is warming faster due to heat transport from oceans in the south

• Warming of the planet is already apparent | 2-3 degrees Celsius on average per century

• The biggest countries with high development contribute most to the climate change

• CO2 is the major contributing factor

• There is a positive correlation between emissions and temperature rise

• There is also a positive correlation between emissions and some other environmental variables, implying the reasons behind their increase

• Emissions are rising and so will the temperature (40 years cause-effect)

• We predicted temperature rise of 1 degree Celsius on average by 2067

• Predicting temperature in the future based on environmental and climate factors is complex and rather theoretical due to the unpredictable cause-effect and concept drift phenomenon

9 - Presentation Link

https://docs.google.com/presentation/d/1Oa9Ndtjd9ca7AzX3M_u1KBwyLaVx-AA5h8u7gwU2Ywg/edit?usp=sharing

References

1. Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1.
2. Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used following decimal, e.g. Version 3.12]. NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ
3. WB Dataset - http://data.worldbank.org
4. Correlation Analysis - http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Multivariable/BS704_Multivariable5.html
5. Climate change impacts on Austrian ski areas, Robert Steiger & Bruno Abegg (Link)
6. HFCs? Curbing Them Is Key to Climate-Change Strategy (Op-Ed), Hallie Kennan, Energy Innovation: Policy and Technology (Link)
7. How do we know more CO2 is causing warming? (Link)
8. Effects of Global Warming [livescience.com]
9. Living Warmer: How 2 Degrees Will Change Earth [livescience.com]
10. In Warming, Northern Hemisphere is Outpacing the South [climatecentral.org]
11. Climate Change: The 40 Year Delay Between Cause and Effect Posted on 22 September 2010 by alan_marshall [climatechangeanswers.org]
12. How long do greenhouse gases stay in the air? [theguardian.com]