Project Motivation

This is analysis for a blog project required in Udacity Data Scientist Nanodegree. It uses several datasets to analyze with respect to the global temperature anomaly.

Medium: Link to Medium Post

Link to Github Repository: https://github.com/ambreen2006/Global-Temperature-Trend

Dataset

• Various datasets are obtained and sources are documented in the reference section.
• Each variable used is explained and analyzed in the notebook Global Temperature Trend.ipynb

Project Files

• Analysis file: Global Temperature Trend.ipynb
• Exported HTML: Global Temperature Trend.html
• Data files: Resource and reference section provide the links for all the dataset.
  • Global Temperature: GLB.Ts+dSST.csv
  • ENSO: enso.csv
  • AOD: aod_annual.csv
  • AGGI: AGGI_Table.csv
  • TSI: TSI composite_42_65_1709.txt
• Image files for concept explanation
  • ENSO_-_El_Niño.png
  • ENSO_Normal.png

Data preparation

• NaN values are removed where applicable
• Data is standardized using standard scaler

Analysis and Modeling

• The output variable's timeline is visualized seperately as well as with explanatory variables
• LinearRegression model is created using scikit to infer coefficients
• Different correlation methods with p-values are considered to understand the variables.

Summary of the Results

• Linear regression model for the global temperature trend was created.
• The average rate of increase in global temperature per decade after 1981 is much higher then what it is since 1880 per decade.
• Greenhouse gases play a significant role in the increasing temperature.
• Stratospheric aerosols cools the temperature.
• A multivariable regression model was created and evaluated.

Resources and DataSets Sources:

[1] Herring, S. C., N. Christidis, A. Hoell, M. P. Hoerling, and P. A. Stott, Eds., 2020: Explaining Extreme Events of 2018 from a Climate Perspective. Bull. Amer. Meteor. Soc., 101 (1), S1–S128, doi:10.1175/BAMS-ExplainingExtremeEvents2018.1.

[2] Herring, S. C., A. Hoell, M. P. Hoerling, J. P. Kossin, C. J. Schreck III, and P. A. Stott, Eds., 2016: Explaining Extreme Events of 2015 from a Climate Perspective. Bull. Amer. Meteor. Soc., 97 (12), S1–S145.

[3] GISTEMP GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed 20YY-MM-DD at https://data.giss.nasa.gov/gistemp/. Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307–6326, doi:10.1029/2018JD029522.

[4] AGGI https://www.esrl.noaa.gov/gmd/aggi/aggi.html) Graphic: The Greenhouse Effect https://climate.nasa.gov/climate_resources/188/graphic-the-greenhouse-effect/ Greenhouse effect https://courses.edx.org/assets/courseware/v1/f40bce9bb2f3570cc65b5303558ab895/asset-v1:SDGAcademyX+CCSI001+3T2019+type@asset+block/Module_1_Reading_5.pdf

[5] TSI ftp://ftp.pmodwrc.ch/pub/data/irradiance/composite/ https://www.pmodwrc.ch/en/home/[6] Interpreting Correlations

[6] Interpreting correlations https://towardsdatascience.com/eveything-you-need-to-know-about-interpreting-correlations-2c485841c0b8

[7] ENSO

Graphics https://en.wikipedia.org/wiki/El_Ni%C3%B1o#/media/File:ENSO_-_normal.svg

Graphics https://en.wikipedia.org/wiki/El_Ni%C3%B1o#/media/File:ENSO_-_El_Ni%C3%B1o.svg

https://www.nationalgeographic.org/encyclopedia/el-nino/ https://www.nationalgeographic.org/encyclopedia/la-nina/ https://psl.noaa.gov/cgi-bin/data/climateindices/corr.pl?tstype1=27&custname1=&custtitle1=&tstype2=0&custname2=&custtitle2=&year1=&year2=&itypea=0&y1=&y2=&plotstyle=0&length=&lag=&iall=0&iseas=1&mon1=0&mon2=11&anom=1&climo1_yr1=&climo1_yr2=&climo2_yr1=&climo2_yr2=&Submit=Calculate+Results https://www.climate.gov/news-features/understanding-climate/el-ni%C3%B1o-and-la-ni%C3%B1a-frequently-asked-questions http://faculty.washington.edu/kessler/occasionally-asked-questions.html#q1 https://www.youtube.com/watch?v=dzat16LMtQk https://psl.noaa.gov/enso/mei/