Lower trophic level marine ecosystem models are highly dependent on the parameter values given to key rate processes, however many of these are either unknown or difficult to measure. One solution to this problem is to apply data assimilation techniques that optimize key parameter values, however in many cases in situ ecosystem data are unavailable on the temporal and spatial scales of interest. Although multiple types of satellite-derived data are now available with high temporal and spatial resolution, the relative advantages of assimilating different satellite data types are not well known. Here these issues are examined by implementing a lower trophic level model in a one-dimensional data assimilative (variational adjoint) model testbed. A combination of experiments assimilating synthetic and actual satellite-derived data, including total chlorophyll, size-fractionated chlorophyll and particulate organic carbon (POC), reveal that this is an effective tool for improving simulated surface and subsurface distributions both for assimilated and unassimilated variables. Model-data misfits were lowest when parameters were optimized individually at specific sites; however, this resulted in unrealistic overtuned parameter values that deteriorated model skill at times and depths when data were not available for assimilation, highlighting the importance of assimilating data from multiple sites simultaneously. Finally, when chlorophyll data were assimilated without POC, POC simulations still improved, however the reverse was not true. For this twophytoplankton size class model, optimal results were obtained when satellite-derived size-differentiated chlorophyll and POC were both assimilated simultaneously.
Abstract. Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20 % random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellitederived data does not pose a significant problem to the assimilation of satellite data into these models. However, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of over-tuning inherent in such complex models.
Understanding of nitrogen cycling on continental shelves, a critical component of global nutrient cycling, is hampered by limited observations compared to the strong variability on a wide range of time and space scales. Numerical models have the potential to partially alleviate this issue by filling spatiotemporal data gaps and hence resolving annual area-integrated nutrient fluxes. In this study, a three-dimensional biogeochemical-circulation model was implemented to simulate the Mid-Atlantic Bight (MAB) nitrogen budget. Model results demonstrate that, on average, MAB net community production (NCP) was positive (+0.27 Tg N/year), indicating net autotrophy. Interannual variability in NCP was strong, with annual values ranging between 0.19 and 0.41 Tg N/year. Along-shelf and across-shelf horizontal transport fluxes were the other dominant terms in the nitrogen budget and were primarily responsible for this NCP variability. The along-shelf current transported nitrogen from the north (0.65 Tg N/year) into the MAB, supplementing the nitrogen entering from terrestrial inputs (0.27 Tg N/year). However, NCP was highest in the year when total water volume transport and inorganic nitrogen input was strongest across the continental slope in the southern MAB, rather than when terrestrial inputs were greatest. Interannual variability of NCP appears to be linked to changes in the positions of the Gulf Stream and Slope Water Gyre. Overall, the strong spatiotemporal variability of the nitrogen fluxes highlights the importance of observations throughout all seasons and multiple years in order to accurately resolve the current status and future changes of the MAB nitrogen budget.Plain Language Summary Portions of the ocean adjacent to land masses play a particularly important role in global nutrient cycling; however, strong spatial and temporal variability in these shallow regions of the ocean make it difficult to quantify nitrogen fluxes from observations alone. Here we use a computer simulation to estimate the fluxes and transformations of inorganic and organic nitrogen in Mid-Atlantic U.S. coastal waters. The coastal circulation flows southward providing roughly two thirds of the inorganic nitrogen to this region, with the remaining third entering from rivers and estuaries. Nitrogen transport across the continental slope is highly variable, directed into the system in some years and out in others. The net community production of the system, that is, the conversion of inorganic to organic nitrogen through photosynthesis minus respiration, is also highly variable. This strong interannual variability is primarily due to the highly variable fluxes of inorganic nitrogen entering this region from across the continental slope where the Gulf Stream approaches the continental slope, rather than the variability in terrestrial inputs. Overall, the strong variability of this region highlights the importance of collecting observations throughout all seasons and during multiple years in order to accurately resolve coastal nutrient budgets.
Abstract. Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower trophic level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20% random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellite-derived data does not pose a significant problem to the assimilation of satellite data into these models. On the contrary, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of overtuning inherent in such complex models.
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