Arctic Carbon Models Don't Add Up

Numerous studies have been released claiming that they can predict at what rate carbon stored in the Arctic may be released in the future if climate change doesn't release its grip. But new research has shown that previous carbon models just don't add up, and that we have yet to truly understand the state of Arctic carbon.

"We all knew there were big uncertainties in our understanding," lead author Josh Fisher said in a statement, but "the results were shocking."

The Arctic has been a major focus of understanding how climate change is impacting the Earth, as it's a region where global warming is hitting hardest. A recent study showed that Arctic ice shrank to its sixth-lowest level on record last week.

Most of this comes from the Arctic's permafrost, which, contrary to what its name implies, is thawing amidst a warming planet and releasing some of its stored carbon.

But how much carbon is leaving this thawing soil and contributing to Earth's greenhouse gas effect?

New research conducted as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) shows just how much work still needs to be done to reach a conclusion on this and other basic questions related to the Arctic.

Fisher, of NASA's Jet Propulsion Laboratory (JPL), and his team analyzed 40 computer models of the amounts and flows of carbon in the Alaskan Arctic and boreal ecosystems. They focused on various factors, such as the rate of plant growth and the amount of carbon exchanged between living organisms and the atmosphere. The researchers found wide disagreement among the models, highlighting the urgent need for more measurements in specific areas from the region.

"If all the models agree with the observations, uncertainty is probably pretty low. If they wildly disagree, uncertainty is pretty high," Fisher said. "The models were all over the board."

Fisher does note that all the models used in the study "are perfectly valid representations of what's going on in the Arctic," however; it's hard to tell which ones come closest to reality.

The researchers hope that CARVE can help identify the strengths and weaknesses of different models, and pave the way for their improvement.