Parameterization of vertical chlorophyll <i>a</i> in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates

Abstract

Abstract. Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for in the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e., 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (Chl a) according to (1) the shelf–offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom, and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface Chl a concentration (Chl asurf; 0.7–30 mg m−3) throughout the open water period, the Chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when Chl asurf is low (0–0.5 mg m−3). By applying our empirical model to annual Chl asurf time series, instead of the conventional method assuming vertically homogenous Chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in Chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e., pre-bloom, post-bloom > 0.7 mg m−3, and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.e., post-bloom < 0.5 mg m−3). SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.

Influence of timing of sea ice retreat on phytoplankton size during marginal ice zone bloom period on the Chukchi and Bering shelves

“…Thereby, we suggest that the surface F L can also be reasonably used to predict the upper layer phytoplankton size structure. This might be due to the shallow bathymetry and SCM depth (∼ 20 m) associated with the nitracline depth (Brown et al, 2015) compared to the higher Arctic Ocean where a deeper SCM (> 40 m) is commonly found (Ardyna et al, 2013). Furthermore, the significantly shallower depth of the PP maximum compared to the depth of the SCM (Fig.…”

“…Hill et al 2005, Ardyna et al, 2013. The omission of the SCM sometimes causes a large error in the satellite estimation of PP eu in the high Arctic (Hill et al, 2013).…”

“…The omission of the SCM sometimes causes a large error in the satellite estimation of PP eu in the high Arctic (Hill et al, 2013). However, Arrigo et al (2011b) and Ardyna et al (2013) showed that the omission of the SCM can lead to only a small error in the PP eu retrieval over most of the Arctic Ocean. Our results supported the latter idea that surface PP explained the variation of PP eu well (Fig.…”

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

“…It should be noted that the similarity could be due to biased seasonal coverage by field studies in ice-free waters, introducing more measurements collected in late summer and fall (i.e., September), thus lowering the ice-free iNPP average (not shown). Alternatively, the similarity in simulated iNPP values in ice-free and ice-influenced areas could result from the averaging of regional differences previously observed in field and satellite-derived NPP data [e.g., Arrigo et al, 2012;Hill et al, 2013;Ardyna et al, 2013] as well as in model simulations [e.g., Zhang et al, 2015]. Recently, Jin et al [2016] showed that simulated under-ice production is also regionally different (i.e., higher in the Arctic shelves, possibly due to enhanced nutrient supply) [Zhang et al, 2015], but not necessarily related to the recent decrease in sea ice concentration, especially in marginal ice zones.…”

Sea ice dynamics influence halogen deposition to Svalbard

“…It seems likely that the greater thickness of Arctic sea ice and its impermeability for almost all of the period (Zhou et al, 2013) inhibits the upwelling of iodine through capillary channels in the ice (Saiz-Lopez and Boxe, 2008), and so atmospheric iodine in the Arctic is instead associated with air flow above ocean surfaces. Considering that the main atmospheric iodine source is biological primary production in colder waters, the iodide concentration is lower (R. Chance, University of York, personal communication, 2013) and so the production of HOI and I 2 from the uptake of O 3 at the ocean surface is less significant, the primary biological bloom occurs around Svalbard during the March-May period (Ardyna et al, 2013), and that iodine is primarily emitted from the open-ocean surface, we can now evaluate the change in sea ice extension around Svalbard during the period covered by the firn core. The iodine profiles from Svalbard show two periods with high concentrations: between 2004-2006 and 2011-2012 where concentrations exceeded 50 pg g −1 (Fig.…”