In May of this year, the UN kicked off a new initiative on climate change – the Big Data Climate Challenge. This initiative is associated with the UN Secretary-General’s 2014 Climate Summit. One of the primary aims of the challenge is to use big data to make the case for climate change action, specifically “to bring forward data-driven evidence of the economic dimensions of climate change.” You can read more about it at the Big Data Climate Challenge (BDCC) website: http://unglobalpulse.org/big-data-climate/.
According to an article at FierceBigData.com, the UN is “looking for any climate-related data projects that show the economic impact of climate change and ways to manage it.” The deadline for BDCC submissions is June 30. Winners will be flown to the United Nations 2014 Climate Summit. The FierceBigData.com article lists examples of relevant domains that can join in multidisciplinary big data efforts to study and manage climate risks. These domains include: smart cities, natural resource management, agriculture and food systems, complex systems, green data centers, material sciences, disaster risk reduction and resilience, architecture and design, behavioral science, climate finance, and carbon markets.
We review here a data science perspective on the problem, by discussing three ways that big data are already embedded in climate change studies:
(1) Big data in climate first means that we have sensors everywhere — in space looking down (via remote sensing satellites) and in situ (“on the ground”) sensors, which are used to monitor and measure weather, land use, vegetation, oceans, cloud cover, ice cover, precipitation, drought, water quality, sea surface temperature, and many more geophysical parameters. These data sets are augmented with correlated data sets: biodiversity changes, invasive species, “at risk” species, etc. These comprehensive data collections give us increasingly deeper and broader coverage of climate change, both temporally and geospatially. This impressive array of sensors also delivers a vast increase in the rate and the number of climate-related parameters that we are now measuring, monitoring, and tracking. All combined, these attributes of climate data satisfy all of the criteria of big data: high volume, variety, and velocity. The combined power of these data sets gives us deeper insights into changes in the biosphere, hydrosphere, cryosphere, and atmosphere, and what is driving change in all of those Earth systems. Two examples of large Earth system monitoring projects are NEON (National Ecological Observatory Network) and OOI (Ocean Observatories Initiative, a project of the Consortium for Ocean Leadership).
(2) Climate change is one of the largest examples of scientific modeling and simulation. These simulations are used to predict climate behavior over the next 100 years, and beyond. Huge climate simulations are now run daily (if not more frequently).