The U.S. has endured 258 weather and climate calamities since 1980, accumulating over $1.75 trillion in damage costs. Other climate-induced disasters, from Australia’s bushfires to floods in Central Africa and vegetation destroying temperatures in Baghdad, have marked 2020 as a pivotal point for climate scientists.
Developments and advancements in climate science hold hugely significant economic, business, and societal implications as the world faces new challenges and risks stemming from climate change. Emerging initiatives revolving around emission reduction, agricultural reformation, and decarbonization have become urgent sustainability agendas for businesses, government agencies, and other organizations.
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Like every entity that relies on data to spark transformative momentum, climate science harnesses the power of data science to unlock new insights that can help evaluate and address critical issues arising from both human-generated climate change and natural climate variables. The end goal is to develop solutions to anticipate, manage, and mitigate them.
Advancements in Climate Science
Climate science, or climatology, emerged from the weather prediction field, and has evolved from the largely numerical based techniques of the 1940’s to the highly sophisticated earth system models used today. Climatologists target long-term trends and patterns, and are largely focused on a universal goal of understanding and acting upon global warming and climate change scenarios.
NASA recently presented a study conducted by researchers from U.C. Berkeley that confirms the accuracy of climate models used over the course of the last half-century in predicting global warming.
Climate modeling has matured thanks to AI, which enables researchers to more accurately forecast environmental responses to variables such as carbon levels in the air that could possibly harm certain populations.
Steven Sherwood, an Australian climate scientist, is one of the global authors of a highly publicized research report on climate change produced by the World Climate Research Program that addresses the trajectory of global warming via climate sensitivity. This research relied on temperature change data, and advanced statistical techniques - the very backbone of data science.
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New Technologies Spark New Potential
Notable AI researchers are contributing to climate science with a roadmap for how ML can help address risks from global warming in domains like climate prediction and forecasting energy demand. AI, computer vision, NLP, Machine Learning, and reinforcement learning are some of the technologies they present as potential interventions in their report. This is just one specific example of how intelligent technologies can be used to facilitate progress in this field.
IBM’s Green Horizon Project and Microsoft’s AI for Earth program aim to use AI and ML to analyze environmental data for critical global climate change initiatives. The two technology behemoths are just a couple of the many companies who are striving to set standards in how digital technologies can help combat climate change.
From a broader perspective, new technologies are offering a fresh take on innovation in crucial areas of climate science:
- ML: Machine Learning algorithms can be used to monitor potentially catastrophic weather events, and predict damages.
- IoT: Can identify and measure indirect impacts of climate change via factors like bee activity, geographical erosion, and water levels.
- Neural Networks: Are being used to separate patterns of forced temperature and precipitation alterations due to human activity within natural climate variabilities.
The Data Difference
Data science has a heroic part to play within the realm of climate science, as discovering new patterns within data will help lay the groundwork for research, solution development, and education.
Data generated from IoT systems, for example, enables climate scientists to develop visualizations about climate change so they can better communicate research and promote understanding.
Using AI technologies will be increasingly important in climate modeling moving towards the future because it speeds up time to insights. This relies on core principles of data science, which uses AI technologies to collect, interpret and analyze data, uncover patterns, and produce predictions and insights.
Interestingly, the natural relationship between data science and climate science has bred a new discipline called climate informatics, which connects the disparities between AI researchers and climate scientists to pursue the promise of technology to answer key questions about climate change.
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The Next-Generation of Data Science
Climate science is helping to shape our awareness and understanding of the social, economic, geographical, and business altering effects stemming from climate change. Data scientists have the opportunity to influence our world alongside the advancement of technology and address some of the most prevalent universal issues of our generation.
Simplilearn provides educational courses and certifications that equip data scientists with the skills and capabilities necessary to augment their career journey in this inspiring and critical field. In particular, check out their PG in Data Science, offered in partnership with Purdue University.
