# Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors
## 3 Results
The period mean of surface ocean DMS concentrations, for the two MPI simulations, was 0.23 nmol L\({}^{-1}\) offset, and the average RMSE for this region was 3.42 nmol L\({}^{-1}\). These two simulations give the same standard deviation from the concentrations benchmark dataset, but the mixed resolution simulation has a slightly lower correlation of 0.61. Warming and loss of Arctic sea ice and increasing biological activity in the open ocean led to higher DMS concentrations. As expected, this process is better represented in the high resolution simulation. By contrast, a high bias was more evident in the tropical Atlantic for the lower resolution configuration. Overall, the results show increasing uncertainty as we progress through a list of biogeochemical processes. Derived products such as net primary production and DMS tend to have lower correlation and higher root mean square errors when compared with nutrients concentration in an ecosystem (Figure 4). The uncertainty further increases as DMS enters the marine boundary layer. In contrast with the level of bias in surface ocean concentrations, DMS fluxes show higher uncertainties between models and the reference dataset. The whole Pacific Ocean, from the tropics all the way to the Bering Sea, displayed more DMS in the model results. This was also the case at the poles, as expected. Spatial correlation for areas below 45\({}^{\circ}\)S was about 0.78 for the three models, with HadGEM2-ES having the lowest RMSE of 2.8 nmol L\({}^{-1}\), and MPI-ESM-LR having the highest at about 3.5 nmol L\({}^{-1}\). The dynamics are different in the Indian Ocean, where DMS fluxes within the gyre dropped below what is obtainable in the referenced dataset. The uncertainty in surface ocean biogeochemistry and sea-air flux increases under Representative Concentration Pathway (RCP) 8.5 as the two CMIP5 models in this study tend to disagree on the direction of DMS flux on the global scale. HadGEM-ES projects a regional scale upswing of over 5 umol m\({}^{-2}\)d\({}^{-1}\) in the Arctic, leading to an overall increase in global mean by 2100 (Figure 5). Global and regional trends have been fairly consistent since the industrial revolution; however, projection from the MPI models agree with a continuous DMS flux increase in Arctic open ocean as temperature increases. The connection between sea ice extent reduction, an increase in primary productivity, and a subsequent increase in marine biogenic aerosols, such as DMS, is more evident in the Arctic. However, the connection between sea-ice trends and aerosol precursor emissions is not definitive in the Southern Ocean. Results for the Southern Hemisphere show a gradual decline in the Southern Ocean flux going into 2100. Increases in global sea surface temperatures will ultimately have a dramatic impact on marine ecosystems, and may influence both primary productivity and the sea-air  
Figure 4: (**a**) Nitrate, (**b**) net primary production, and (**c**) dimethylsulfide(DMS) spatial Taylor diagrams. Benchmarking datasets for the variables are listed in Table 1.  
Figure 3: (**a**) Temporally integrated period mean and (**b**) bias of DMS Model Flux (DMF) for HadGEM2-ES, MPI-ESM-MR, and MPI-ESM-LR between 1978–2008. flux of marine aerosols. To determine the degree of influence of fundamental biogeochemical processes and validate future predictions, we continue to develop inherent metrics in IOMB.