Decadal basin-scale changes in diatoms, dinoflagellates, and phytoplankton color across the North Atlantic

The Continuous Plankton Recorder (CPR) survey has been used to characterize phytoplankton and zooplankton space-time dynamics in the North Sea since 1931 and in the North Atlantic since 1939. Phytoplankton biomass is assessed from these samples by visual assessment of the green color of the silk mesh, the Phytoplankton Color Index (PCI), and the total count of diatoms and dinoflagellates. Species with a frequency of occurrence greater than 1% in the samples are used as indicator species of the community. We investigated (1) long-term fluctuations of phytoplankton biomass, total diatoms, and total dinoflagellates; (2) geographical variation of patterns; (3) the relationship between phytoplankton and climate forcing in the North Atlantic CPR samples; (4) the relative contribution of diatoms and dinoflagellates to the PCI; and (5) the fluctuations of the dominant species over the period of survey to provide more information on the processes linking climate to changes in the phytoplankton community. As a result of the differences in microscopic analysis methods prior to 1958, our analyses were conducted for the period ranging from 1958 to 2002. The North Atlantic was divided into six regions identified through bathymetric criteria and separated along a North-South axis. Based on 12 monthly time series, we demonstrate increasing trends in PCI and total dinoflagellates and a decrease in total diatoms.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-abased models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.