Since 1765, the ocean has soaked up a sizeable chunk—approximately 30%—of the excess carbon dioxide released into the atmosphere by human activity. As such, modeling future climate change requires that researchers accurately understand carbon dioxide uptake by the ocean. However, climate projections from current simulations are often hindered by uncertainty stemming from unknowns in future climate variability and emissions and the inherent limitations of modeling the physical world.
To get a handle on just how much uncertainty is present in climate projections, researchers often run multiple simulations with the same external forcing acting on various initial atmospheric inputs. These large ensembles of simulation are common for atmospheric projections but have been used less frequently to make predictions about the state of the ocean.
Here Lovenduski et al. present a new analysis of ocean carbon dioxide uptake from ensembles of Community Earth System Model simulations, which allowed the researchers to account for uncertainty arising from differing emissions scenarios or climate variability. For climate variability, the authors considered natural cycles like the El Niño–Southern Oscillation, which have the potential to dampen or amplify trends in ocean uptake. To account for uncertainty due to the model structure, the team looked at simulations from the fifth phase of the Coupled Model Intercomparison Project. They covered much of the globe and 17 distinct biogeographical biomes.
On global scales, future changes in the exchange of carbon from the air to the sea are dominated by uncertainties in the emissions themselves; emissions depend on complex changes in society and technology, which are naturally hard to predict. Uncertainties in internal variability disappear within a lead time of a decade or so, and model uncertainties disappear after the first few decades, leaving the emissions uncertainties to dominate in the long run. On regional scales, however, future changes are dominated by internal variability and model structure.
The authors suggest that scientists invest their resources in improving model structural uncertainty for two reasons: Improving upon model structure will be easier than guessing what future emissions will look like or better understanding the climate system’s natural variability, and it will greatly reduce the uncertainty on a regional scale. (Global Biogeochemical Cycles, doi:10.1002/2016GB005426, 2016)
—Shannon Hall, Freelance Writer