“We are the first generation to be able to end poverty and the last generation that can take steps to avoid the worst impacts of climate change. Future generations will judge us harshly if we fail to uphold our moral and historical responsibilities. “– Ban Ki-moon, Former-Secretary General UN.
Do you know that climate change caused nearly 70% of all the extreme weather events in the last 20 years?
Global warming refers to an increase in the average long-term temperature of Earth’s climate. The expansion of physical factors, such as CO2, CH4, and N20, has triggered a substantial shift. The higher emission of greenhouse gases has led the Earth’s temperature to rise sharply since 1990.
Compared to 100 years ago, the average temperature of Earth is 1 degree Celsius higher. Climate scientists fear that in the next 200 years, the average global temperature will rise by almost 6 degrees Celsius.
To handle global warming, countries need an effective action plan. For this, they’re heavily relying on real-time data analytics. Predictive analytics offers considerable promise in predicting global warming trends.
There is a reason why climate scientists are increasingly leveraging data analytics and machine learning. According to research, these models are more affordable and accurate in the following scenarios:
- When there are large amounts of data, but traditional statistics are not significant enough to model systems.
- When there are good models, it costs an arm and leg to compute them through conventional production methods.
Climate scientists have used data analytics to identify pollutant sources, classify crop cover, and calibrate satellite sensors. Deep learning, one of the critical branches of machine learning, could help with super-resolution, pattern recognition, and forecasting in global warming, and compile data of environmental imagery to speed up data analytics in the field.
Both public and private sector organizations have been creating cutting-edge tools and technologies to fight against global warming. Voluminous amounts of data of various variables, such as carbon emissions, forest cover, sea levels, and temperature change, are stored and analyzed in real-time. These tools can identify the correlation between variables, recommend actionable insights, and generate patterns and predictions. In this way, appropriate proactive precautions or actions can be addressed timely.
Making better predictions
The push to adopt data analytics builds on the work performed in climate informatics, a field developed in 2011 that merges climate science and data analytics. This discipline covers a wide range of topics, including enhancing prediction of extreme events, such as floods, paleoclimatology — reimagining historical climate conditions by using data extracted from things, such as climate downscaling, ice cores, and utilizing large-scale models to make predictions on a hyper-local level and study the socio-economic effects of climate and weather.
Data analytics can generate hidden and valuable insights from the massive amounts of problematic climate and global warming simulations created by climate modeling.
One of the earliest climate change simulations developed at Princeton University in the 1960s, these models represent ice, cryosphere, land, oceans, and atmosphere. Despite the agreement on basic scientific assumptions, Claire Monteleoni, a computer science professor from the University of Colorado, Boulder, is not satisfied with their accuracy, especially for long-term forecasts. She said, “There’s a lot of uncertainty. They don’t even agree on how precipitation will change in the future.”
Monteleoni has used data analytics to combine the predictions of around 30 climate models for making better predictions.
How is data analytics helping against global warming?
Data analytics can counter the threat of global warming. One of the prominent examples is the work done by Climate Central, an independent non-profit organization. They developed Surging Seas, an interactive map that shows the information on the rising sea levels in the U.S. Open the map; you can observe accurate sea levels in different areas, view historical data, action plans, and flood warnings. The tool predicts that Miami Beach will go underwater due to rising sea levels shortly.
Illegal logging is one of the primary factors causing deforestation. Rainforest Connection (RFCx) utilizes data analytics and cell phones to control deforestation. They created acoustic monitoring systems to safeguard a rainforest area, enabling them to improve their responses via real-time alerts. RFCx works with TensorFlow, a machine learning framework, to analyze forest sounds in real-time and identify sounds that resemble logging trucks, chainsaws, and similar illegal activity noises to locate forest issues.
Maria Uriarte, a professor of Environment Biology from Columbia University, and Tian Zheng, a Statistics professor at the Data Science Institute, used data analytics and AI to examine Hurricane Maria’s impact on the El Yunque National Forest in Puerto Rico. The study’s motivation was to find how tropical storms influence tree species’ distribution and notice their influence on global warming and climate change.
Due to Hurricane Maria‘s extensive damage, the only viable way to find the affected species was to go through countless high-resolution images. Nevertheless, a particular dilemma existed: how to distinguish one species from another when only a green mass was visible in a large area?
They used AI and data analytics to analyze the high-resolution images and compared them with a dataset — it identified each tree species in given plots and mapped them accordingly. The ground data from fixed fields helped them determine how different species looked from above in the aerial images.
Figuring out how modern storms affect the composition and distribution of forests is necessary for global warming. When a hurricane damages forests, it forces the vegetation to decompose, emitting more significant amounts of CO2 into the atmosphere.
As trees grow again in the storm’s aftermath, they store less carbon due to their smaller size. Hence, as climate change causes more storms, lesser carbon will be stored, and more will be released—ultimately worsening global warming.
Data analytics can play a pivotal role in creating more livable and sustainable cities. It can enhance a city’s energy efficiency by processing data collected from IoT equipment, such as smart meters. In this way, it can forecast energy demand.
Smart solutions can empower authorities to simulate potential zoning laws, build flood plains, and work on disaster preparedness and urban planning. A sustainable city’s administration can envision a state-of-the-art analytics dashboard that shows real-time data on energy use, water availability, weather, and traffic to make cities more livable and efficient.
In China, the Green Horizon project (developed by IBM) can monitor pollution sources, forecast air pollution, and create potential plans. For instance, it can suggest whether it is better to close specific power plants or limit drivers to minimize pollution levels within a particular area. IBM is working on another system to aid cities with predicting future heat waves.
It would simulate the climate at the urban scale and study a diverse set of strategies to test how they ease heat waves. For instance, data analytics and machine learning models could determine the best areas to plant new trees to enhance optimal tree cover and reduce heat from the pavement.
A world without data analytics
Without data analytics, plans, or policies for tackling global warming becomes one-dimensional. Hypothetically, possible scenarios include:
- The calculations for reducing carbon emissions could be affected. Consider a system where companies unanimously agree to enforce a law that requires carbon emissions to minimize carbon emissions produced by industrial plants, cars, and other sources by 3% in the next ten years. However, the actual requirement was to cut carbon emissions by 6%, and thus flawed calculations will exacerbate global warming.
- Glaciers are melting rapidly, and sea levels are rising faster than ever, placing coastal regions under serious risk. If predictive analytics is not applied, the relevant authorities could fail to take proactive housing relocation and rehabilitation planning steps.
At present, there’s no doubt that data analytics is redefining climate change policies. Today, it has become a core component of plans and procedures designed to address and predict global warming trends. Data analytics’ potential for analyzing large amounts of complex climate data, identifying hidden correlations, and displaying real-time insights is essential.
However, data analytics can only do so much. Ultimately, it’s up to the concerned authorities to implement it for the greater good.
Remember, there’s no planet B.