Correcting in situ chlorophyll fluorescence time‐series observations for nonphotochemical quenching and tidal variability reveals nonconservative phytoplankton variability in coastal waters

Abstract

Chlorophyll fluorometry is one of the most commonly implemented approaches for estimating phytoplankton biomass in situ, despite documented sources of natural variability and instrumental uncertainty in the relationship between in vivo fluorescence and chlorophyll concentration. A number of strategies are employed to minimize errors and quantify natural variability in this relationship in the open ocean. However, the assumptions underlying these approaches are unsupported in coastal waters due to the short temporal and small spatial scales of variability, as well as the optical complexity. The largest source of variability in the in situ chlorophyll fluorometric signal is nonphotochemical quenching (NPQ). Typically, unquenched nighttime observations are interpolated over the quenched daytime interval, but this assumes a spatial homogeneity not found in tidally impacted coastal waters. Here, we present a model that provides a tidally resolved correction for NPQ in moored chlorophyll fluorescence measurements. The output of the model is a time series of unquenched chlorophyll fluorescence in tidal endmembers (high and low tide extremes), and thus a time series of phytoplankton biomass growth and loss in these endmember populations. Comparison between modeled and measured unquenched time series yields quantification of nonconservative variations in phytoplankton biomass. Tidally modeled interpolation between these endmember time series yields a highly resolved time series of unquenched daytime chlorophyll fluorescence values at the location of the moored sensor. Such data sets provide a critical opportunity for validating the satellite remotely sensed ocean color chlorophyll concentration data product in coastal waters.

Cardinal Buoys: An Opportunity for the Study of Air-Sea CO2 Fluxes in Coastal Ecosystems

“…Conversion of in situ fluorescence into Chl-a concentrations has always been challenging, with fluorescence influenced by numerous factors: heterogeneity of the phytoplankton community structure across the year (Southward et al, 2005;Guilloux et al, 2013), phytoplankton taxonomy (Proctor and Roesler, 2010), cell size (Alpine and Cloern, 1985), pigment packing (Bricaud et al, 1983(Bricaud et al, , 1995Sosik et al, 1989;Sosik and Mitchell, 1991) and the effect of non-photochemical quenching (Xing et al, 2012). Despite these limitations, Chla concentrations remains a suitable proxy for phytoplankton biomass (Carberry et al, 2019). The standard deviations obtained on the residuals were close to those obtained with similar sensors on the Armorique Ferry Box between Roscoff and Plymouth (Marrec et al, 2014), therefore no further corrections were applied to the converted Chl a signal.…”

Ubiquitous Patchiness in Chlorophyll a Concentration in Coastal Archipelago of Baltic Sea

“…In principle, such information can be derived from empirical data by using phenomenological models. While they can deal with the variability of resolution among different geospatial datasets, the accuracy of these approaches is challenged by the complexity and diversity of the underlying processes (Carberry et al, 2019). In short, the observed outcome is a result of physical, chemical and biological interactions (McGillicuddy, 2008).…”

“…Moreover, living planktonic organisms actively move in the flow field. Although some of these processes have successfully been incorporated in phenomenological models (Carberry et al, 2019), they still rely on inherent assumptions about the relative contribution of most of the underlying processes.…”

“…Bathymetry, tides, and currents combine freshwater runoff and marine surface and deeper waters over small spatial scales to create considerable spatial complexity in hydrography, mixed layer depth, and nutrient content, driving spatial complexity in phytoplankton biomass. Furthermore, short temporal variability, similar in timescale with that of phytoplankton physiological variability, is induced by advective currents and wind-driven mixing (Carberry et al, 2019). Hence, disregarding the patchiness of Chl-a in assessments of the trophic state of coastal ecosystems is likely to result in spatiotemporally skewed or overgeneralized interpretation of their properties.…”

“…On top of these proximate causes, the optical complexity of coastal waters in the form of other compounds (e.g., DOM and non-living particles) that fluoresce at similar wavelengths as chlorophyll a will further distort in situ fluorescence measurements. Although in situ observations can be calibrated using discrete water samples, the work is often logistically too challenging due to match up issues (Carberry et al, 2019).…”