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Guest post: A secondary school student's perspective on tackling climate challenges

Monday, December 7, 2020

Alex Kwang is a student at St Paul's School in London who, during lockdown, started a self-led project on predicting extreme rainfall events in Rwanda. The aim of Alex's research was to provide better information for decision making around disaster prevention and agricultural planning in response to climate change. In this guest article, he shares his experiences.


The Holy Grail of Climate Change

Let’s take a moment to consider the climate system: the interaction of two fluids on a rotating spheroid heated by the sun. One vital component of the atmosphere is water in its liquid, solid and gaseous phases. The changes in these phases have vast ramifications on the energy balance of the system. Another such component is the concentration of CO2, which will have a 2% perturbation on the same energy balance when doubled. Various changes in oceans, clouds and other features also affect this balance.

In this complex system, what is the likelihood that changing a 2% perturbation in energy will be the magical solution to the entirety of climate change and its effects around the globe?

I do not know whether there exists a holy grail of climate change, but there are 12 million people in Rwanda desperately in need of that holy grail. 

Why Rwanda?

In any crisis, it is always the most vulnerable who suffer the greatest impacts. Despite only contributing 0.4% of the greenhouse gas emissions1, Rwanda bears the brunt of the impact of climate change when compared to western countries. Considering that the agriculture sector employs 62% of the labour force and contributes 30% of the GDP1, climate change becomes all the more threatening.

Access to early weather information plays a critical role for farmers to make informed choices. However, farmers in Rwanda lack such crucial information. In 2019 alone, extreme flooding wiped out 15,264 houses, damaged 9,412 hectares of crops and killed 797 livestock2. The 10-day climate bulletin sent out by the National Meteorological Service had arrived late to most rural areas, rendering citizens clueless to the incoming disaster. 

The natural question would be “As a high school student, what can I do to develop better warning systems to a country with 39% of the population3 living below the poverty line?” As an aspiring scientist/mathematician, I wanted to harness my mathematical modelling skills and apply them to solve a real-life issue. Where better to start than trying to design a mathematical model? This was the reason why I decided to research and predict long-term extreme rainfall in Rwanda. 

As I learnt more about climate models, I discovered that I lacked the depth of knowledge in physics and atmospheric science to code up complex dynamical climate models. Not to mention, most of the existing theoretical climate models used by researchers were inaccessible to a high-school student. Therefore, open-source models were my first port of call.

Initial Setbacks

Open-sourced models are widely available, but the physics concepts involved were still daunting for me. Hours of chasing research citation links and navigating institutional archives yielded the existence of various open-source climatic models, such as the ISCA model and the Apache Climate Workbench. While they gave me a structure to understand the fundamental components of the climate system, the sheer gap in knowledge remained something seemingly insurmountable.  

Another roadblock during my research was the accessibility of reliable data. The ideal scenario was having both satellite and ground data of precipitation in Rwanda. The Columbia IRI/LDEO database, a massive online compendium of climatic datasets, provides both satellite and ground data. Nevertheless, the crucial ground data for precipitation, available through the Rwanda Meteorological Agency, was locked behind institutional access.

Unable to make headways, it was time to cold call and reached out to the experts. Email after email, researcher after researcher, I hoped to obtain guidance on what datasets are reliable and where I should begin searching. My efforts were not in vain. From the researchers who got back to me, I was delighted to hear that it was common to work without ground data. Through Professor Mutter at Columbia University, I learnt about the importance of contextualising my knowledge before beginning to brainstorm my research question; Dr Declan Finney from the University of Leeds directed me and guided me to begin understanding the specialised knowledge. These were some of the highlights from scientists in my research journey. Their guidance inspired the scope of my project and helped my research take shape. Among these scientists was Professor Rosalind Cornforth from the Walker Institute. She expanded my research horizon by introducing to me the concept of need-based scientific research.

Research to Address Real Needs

Every day, the farmers in Rwanda live and breathe in the fright of their lifetime’s work being ruined by ten stormy days. However, with the magnitude of uncertainty present in current scientific research, we are unable to convey all the necessary information to people in need. Instead of tackling the issue head-on, the best way is to seek the root of the problem: the specific needs of different stakeholders.

Only through need-based research can we understand the true depth of the plight of the people. It puts meaning into science as I witness its transformative power at every level of the societal hierarchy. Bringing fundamental science to policy formation, and a general level of scientific and numerical literacy to help NGOs, institutions and stakeholders understand fundamental scientific discoveries and their significance is the most prominent avenue of change for Rwanda.

After finishing my research, my next step is to use my model and develop an app. This app aims to address the climate information gap and meets the needs of Rwanda’s farming population. Harnessing my Python programming skills along with some self-study in app development and machine learning, I hope to expand my research beyond the academic realm and transform mathematical models into practical usage for farmers. 

My Main Takeaway

This independent research experience was undoubtedly one of the challenges which required the most perseverance from me, but I’ve learnt a lot in this process as well. As I reached out to different scientists and listened to their stories in the field, it opened up my eyes to the inequality of climate change. Through this experience, I want to get involved in helping countries like Rwanda through small projects that I take on.

But first, I plan to study climate science at university. Only when I have mastered the component fields will I truly immerse myself in opportunities to cross-pollinate ideas from these different disciplines. Through collaborating with an interdisciplinary team, I want to be at the forefront of addressing complex societal problems like climate change.


The research report can be found here.






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