Modeling the Planet’s Future

Arizona State University professors pair human factors with Earth science models

reed24

Nature and humanity are more co-dependent now than ever, according to researchers at Arizona State University (ASU), where a paradigm shift in predictive modeling is underway.

According to ASU professors Sander van der Leeuw and Michael Barton, the exclusively natural approach taken by standard Earth science models is no longer sufficient. Human impact on Earth has become too significant to ignore and it’s time for Earth science research and analysis to evolve accordingly.

The solution proposed by these ASU experts and their outside collaborators is AIMES 2.0, a new global forecasting plan that will incorporate distinctly human social systems such as the internet, finance, governance, and demography with data from traditional Earth science fields like geology, astronomy, and meteorology.

An ASU SHESC press release compares AIMES 2.0 to “a data-driven version of The SIMS.” Researchers will input variables such as overpopulation, regime change, and greenhouse gas accumulation to predict how humanity would fare under the potential circumstances of the future and recommend sustainable solutions.

According to the press release, “this includes establishing shared computational software, development and implementation standards, and even common research agendas.”

This new methodology is gaining popularity on a global scale; it has already been adopted as a part of international projects Future Earth and The World in 2050, which has received support from NASA.

Photo Credit: Benjamin Reed photography via ASU.edu

Maps for a Vaccine Distribution

GIS mapping capabilities are essential to an equitable and speedy distribution of a COVID-19 vaccine

, ,

A National Strategy for Critical and Emerging Technologies

New White House strategy outlines ways to protect the nation's competitive edge in world-changing emerging technologies

, ,

Measuring the Earth’s Magnetic Field

NGA called upon solvers to submit novel approaches to geomagnetic data collection for WMM