Ocean acidification prediction now possible years in advance
New study relies on massive NCAR dataset
May 4, 2020 - by Staff
A composite image of blooming phytoplankton and swirling currents along the coast of California and western Mexico. (Image: Norman Kuring/NASA)
This article is based on a news release from the University of Colorado.
A team of researchers has developed a method that could enable scientists to accurately forecast ocean acidity up to five years in advance. This would enable fisheries and communities that depend on seafood negatively affected by ocean acidification to adapt to changing conditions in real time, improving economic and food security in the next few decades.
Previous studies have shown the ability to predict ocean acidity a few months out, but this is the first study to prove it is possible to predict variability in ocean acidity multiple years in advance. The new method, described today in Nature Communications, offers potential to forecast the acceleration or slowdown of ocean acidification.
“We've taken a climate model and run it like you would have a weather forecast, essentially – and the model included ocean chemistry, which is extremely novel,” said Riley Brady, lead author of the study and a doctoral candidate at the University of Colorado Boulder.
The model runs used for the study are part of the Decadal Prediction Large Ensemble at the National Center for Atmospheric Research (NCAR). The massive dataset was created using the NCAR-based Community Earth System Model (CESM). For each year between 1954 and 2015, scientists used historical observations to create initial conditions for 40 model simulations and then allowed the simulations to run forward 10 years. The result is a trove of data that scientists can analyze to look for phenomena that might be predictable on scales of years to a decade.
"This study demonstrates that Earth system models can be used to not only forecast climate variables but also to make predictions relevant to ecosystems," said NCAR scientist Matthew Long, a co-author of the paper. "Ultimately as these models mature, such predictions could yield valuable information for fisheries and resource managers, enabling probabilistic risk forecasts and proactive, advanced management actions."
Read more about the study in a news release from CU Boulder.