# Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors
## 3 Results
The observational datasets used to estimate bias are described in Table 1.  
Using IOMB, we estimate a total value of 30.6 Tg of sulfur transferred from the ocean into the MBL annually in the form of DMS gas. This is very close to the integrated value of 28.1 (17.6-34.4) Tg of S year\({}^{-1}\) provided for the same period in [PERSON] et al. (2014). The difference in global average emissions could well be traced back to the temperature field, which should affect the aqueous diffusion coefficient of the molecule. We also plan to investigate the diversity of the implied sea-air transfer schemes. DMS Computed Flux (DCF) for HadGEM-ES, MPI-ESM-MR and MPI-ESM-LR stand at individual levels of 21.1 Tg of S year\({}^{-1}\), 22.3 Tg of S year\({}^{-1}\), and 23.8 Tg of S year\({}^{-1}\) in IOMB. However, intrinsic fluxes from the various ESMs; which we refer to as the DMS Model Flux (DMF), predict higher emissions of 34.3 Tg of S year\({}^{-1}\), 39.2 Tg of S year\({}^{-1}\), and 42.3 Tg of S year\({}^{-1}\), respectively. Again, there will be the need to investigate other transfer mechanisms, along with biases in wind fields. It is also the case that at this early stage in the research, we have not yet considered in any direct way the cooling feedback effect of excess natural sulfur. This will generate considerable uncertainty in model outcomes through regional to global radiative forcing. What can be done working from the IOMB results presented here is to recommend regular marine aerosol checks, in order to place more realistic biogeochemical constraints on climate models. The IOMB absolute scores in both DMF and DCF analyses are almost identical (Figure 1), with DCF having a slightly higher absolute score due to the absence of atmospheric dynamics. Uncertainty in atmospheric processes represented in ESMs increases the overall DMS flux bias, the temporal bias, and RMSE, thus lowering the absolute score. Overall, DMF tropical Pacific sea-air flux rises to over 20 \(\upmu\)mol m\({}^{-2}\)d\({}^{-1}\) from about 8 \(\upmu\)mol m\({}^{-2}\)d\({}^{-1}\), as estimated in the benchmark dataset (Figure 3). For the HadGEM-ES model, the global period mean is 5.43 \(\upmu\)mol m\({}^{-2}\)day\({}^{-1}\), with a bias of 1.2 \(\upmu\)mol m\({}^{-2}\)day\({}^{-1}\). The positive bias reported in surface ocean DMS concentration propagates into the marine boundary layer with the models over-predicting the flux in the tropical Pacific and at high latitudes. Other than the regional offsets in the Greenland and Chukchi seas, simulations tend to match the referenced dataset within the Arctic circle. Predictions in the Southern Ocean were higher across all of the models by an average of 3 \(\upmu\)mol m\({}^{-2}\)day\({}^{-1}\). Fluxes in the Weddell and Ross seas were slightly lower, but the high spatial bias in net primary production for this region suggests that representation of the happophytes might be a contributing factor. This is also evidenced by the low model concentrations of DMS. The poleward shift of DMS distribution was mainly absent in the DCF benchmarking result, where fluxes match perfectly with the spatial trends in surface ocean concentration. This is due to the absence of atmospheric transport process. The MPI-ESM model provides an opportunity to analyze the impact of spatial resolution in surface concentrations and sea-air fluxes. The IOMB variable scores for the two resolutions were similar except for silicate, which is an important nutrient for diatoms in making their hard exteriors. The most important disparity in silicate concentration lies in polar regions, where the lower resolution model shows a high silicate bias in the Norwegian Sea and the Weddell Sea relative to the WOA dataset. A significant depletion of Southern Ocean surface nitrate concentrations as a result of increased carbon fixation was more pronounced in these simulations than in the HadGEM model (Figure 2).