Some 300 million years ago, Earth had one continent called Pangea. Over millions of years, that vast single land mass broke up and drifted in different directions, creating the seven continents that exist today.
Since the planet changed so dramatically over millennia, it raises an obvious question: How will it change in the future? The same forces, plate tectonics and continental drift, that broke up Pangea hundreds of millions of years ago still exert themselves. Scientists predict that in the next 250 million years we’ll return to the same mega-continental construction, this time called Aurica.
The predicted formation of a new supercontinent far, far into the future is just part of the work that the NASA Goddard Institute for Space Studies (GISS) and other institutions around the world, including the University of Lisbon, Portugal, and Bangor University, U.K., are doing. And as you might guess, coming up with a model of what the future supercontinent will look like — or how climate change will affect our lives — requires data and computing power. Lots of it.
Modeling the distant and the immediate future
Few organizations can crunch the kind of volume of information required to do advanced climate modeling, but thankfully the NASA Center for Climate Simulation (NCCS) is one of them.
The NCCS provides high-performance computing (HPC) for NASA-sponsored scientists and engineers to perform climate research, all in hopes of gaining a better understanding of the changes currently taking place and the effect those changes may have on the future of the planet.
One of the tools available to researchers is the Reanalysis Ensemble Service (RES), which lets users perform queries on data about the Earth’s surface and atmospheric conditions. RES, according to NASA, addresses big data challenges for climate scientists.
“As the availability and volume of Earth data grow, researchers spend more time downloading and processing their data than doing science,” according to the NCSS website. Making the RES high-performance big data analytics framework available to researchers lets them “leverage compute power to analyze large datasets located at the NCCS through a web-based interface, thereby eliminating the need to download the data.”
RES leverages Cloudera for backend analytics of their climate research data, allowing researchers to derive insights from the climate data stored and processed by RES. The combination of a scalable cloud infrastructure, Python scripts, and open-source tools enables researchers to make sense of the rapidly increasing volumes of climate data that NASA accumulates.
In addition to the creation of models of the Earth’s surface millions of years into the future, climate data is helping scientists understand the more immediate effects of climate change. Data analysis focused on rising sea levels, the melting of polar ice, and the growing intensity and diversity of storms unlock insights that guide governments, corporations, and society at large on how to deal with climate change.
In Europe, for instance, this data is driving a strong sustainability effort to create a carbon-neutral continent.
Achieving sustainability goals with big data tools
It’s not just scientists and climate activists who are pressuring their governments for urgent changes. Companies around the world are now expected to meet sustainability goals.
And Cloudera plays a role there too by helping organizations understand how climate and the financial markets intersect. To that end, banks and financing institutions use the Cloudera Data Platform to measure the effect of climate-related risks on investment activities.
As explained by Joe Rodriguez, senior managing director of financial services at Cloudera, Cloudera works with simulation technology partner Simudyne to generate scenarios that help companies understand their exposure to climate risks.
Climate research is a global effort with the involvement of countless entities in the private, public, and non-profit sectors. The use of Cloudera tools helps make the effort a little easier by providing scientists and researchers with data insights that otherwise might take a lot longer to achieve.
