Understanding Climate Change Using Data Science
Climate Change and Our Responsibility
Preface
Sahasra Chava
2020, Junior, Poetry & Spoken Word
Honorable Mention at Bow Seat Ocean Awareness Programs
- - - - -Poisoning, staining, killing the animals. The waste
Trapping, cutting, killing the animals.
When our momentary song of convenience fades,
The unborn blood spills on these lands and seas,
mingles with their shedding:
Painting a collective portrait of our manifold decay.
helpless in their erasure—
How many more until we care?
Fighting for the land and seas
which was theirs all along;
Sip after sip from the poisoned glass of greed
numbs our humanity; as we
cast its shards into the sea.
The land and water count down for us:
Do you hear?
advancing in a race to un-blue
this planet, this still-blue planet.
and shun our plastic apathy.
Look, hope still stirs in this half-full chalice:
Drink from it and dream.
Hand in hand, we can reclaim
The oceanic blue and the algal green,
The coral chrome and the anemone red.
Recycle, Reduce, Reuse,
Clean, Donate, Volunteer—
So many ways, so little time.
Let the waves catch you.
- - - - -Climate change is the defining issue for our generation. If we don’t address it, it will continue to be a critical issue for the future generations to come. Every time, we open a newspaper or a website or any social media app, there are stories about climate change. There are strong opinions about climate change and what needs to be done about it.
Our interest in the subject started just before the pandemic. We read the news about teenage climate activist Greta Thunberg, time person of the year for 2019, and her journey across the Atlantic to bring awareness to the dangers of climate change.
We got curious about the impact of climate change and started reading about the topic. This is also the time we started experimenting with Python and visualization. The classes we were taking at school including statistics, environmental science and programming motivated us to learn more about how the climate is changing using data.
We have decided to create some simple visualizations to understand some of the issues and also to practice our programming skills. We found them useful for our own learning. Our ability to change the data or the variables and see what is happening around us in a nice visualization excited us. We can understand a bit more about how to use data, how to visualize data and draw some insights from these visualizations.
We acknowledge that we knew little before we started the book and even now, we know little about this vast, complicated and complex subject of climate change. We feel that we just know a bit more now than we did before.
We have tried to learn about the climate change and understand the issues involved. There is so much information available over the web. There are probably tens of thousands of information sources and analysis from government websites, multinational agencies, non-profit organizations, academic labs and publications. We were overwhelmed with the amount of information and the scientific terminology. For example, The state of the climate report for 2022 is more than 500 pages with hundreds of contributors and thousands of technical terms.
The data sources, S276–S501 cited in the report are themselves around 25 pages!
This is our limited attempt at making sense of the vast amount of data and to understand the issues related to climate change. As we tried to make sense of the data and topics involved, we started putting together these in Jupyter notebooks for our own understanding.
It helped us to make sense of these topics. We hope it is useful for others in understanding climate change better.
We are not programmers. We learned programming by writing the code in the book. So, the code quality is going to be uneven! We hope we got better towards the end!
We made a number of mistakes in figuring out the code. We googled extensively, tried to learn from Stack Overflow and other resources.
ChatGPT wasn’t available when we started writing the book in 2020. Most of the code was written without the help of ChatGPT and other AI programming assistants.
We experimented with ChatGPT in 2023 but the code it gave didn’t always work. We realized that knowing how to code helped us modify the ChatGPT code when needed. If we blindly took the code from ChatGPT, it may not run. Also, it wouldn’t have helped us with our aim to learn how to code in Python.
Of course, the code in the book could have probably written more cleanly, more elegantly and may be more efficiently. But we decided to keep it as is, reflecting our learning journey and the mistakes that we made in the process.
As we were revising the book, we have realized that we could use AI programming assistants to help us document the code more effectively. So in parts of the book, during our revision, we used the step-by-step documentation of our code from Microsoft Copilot with suitable modifications. We found the explanations very helpful, and we encourage you try it!
Our goal is to combine data and programming to generate visualizations that give us a better sense of the ongoing climate change.
Please let us know if you find any bugs or explanations that are not clear. Also reach out to us if there are additional climate change related topics and data sources that you would like us to explore. We welcome your feedback on the exposition, code, or any other aspect of the book. Your input is valuable to us.
We are excited to share our learning journey with you! We hope to improve the book with your constructive feedback!
Got feedback? Found a bug (there may be many :()? Are there more issues that we should be covering? You can reach us at nextgen360ga@gmail.com. Thank you in advance!
Sahasra Chava and Sloka Chava