Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr ${}^{-1}$ over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive.

Primary production and photoacclimation models are two important classes of physiological models that find applications in remote sensing of pools and fluxes of carbon associated with phytoplankton in the ocean. They are also key components of ecosystem models designed to study biogeochemical cycles in the ocean. So far, these two classes of models have evolved in parallel, somewhat independently of each other. Here we examine how they are coupled to each other through the intermediary of the photosynthesis–irradiance parameters. We extend the photoacclimation model to accommodate the spectral effects of light penetration in the ocean and the spectral sensitivity of the initial slope of the photosynthesis–irradiance curve, making the photoacclimation model fully compatible with spectrally resolved models of photosynthesis in the ocean. The photoacclimation model contains a parameter θ m , which is the maximum chlorophyll-to-carbon ratio that phytoplankton can attain when available light tends to zero. We explore how size-class-dependent values of θ m could be inferred from field data on chlorophyll and carbon content in phytoplankton, and show that the results are generally consistent with lower bounds estimated from satellite-based primary production calculations. This was accomplished using empirical models linking phytoplankton carbon and chlorophyll concentration, and the range of values obtained in culture measurements. We study the equivalence between different classes of primary production models at the functional level, and show that the availability of a chlorophyll-to-carbon ratio facilitates the translation between these classes. We discuss the importance of the better assignment of parameters in primary production models as an important avenue to reduce model uncertainties and to improve the usefulness of satellite-based primary production calculations in climate research.

The paper investigates the wintertime dynamics of the coastal northeastern Adriatic Sea and is based on numerical modelling and in situ data collected through field campaigns executed during the winter and spring of 2015. The data were collected with a variety of instruments and platforms (acoustic Doppler current profilers, conductivity–temperature–depth probes, glider, profiling float) and are accompanied by the atmosphere–ocean ALADIN/ROMS modelling system. The research focused on the dense-water formation (DWF), thermal changes, circulation, and water exchange between the coastal and open Adriatic. According to both observations and modelling results, dense waters are formed in the northeastern coastal Adriatic during cold bora outbreaks. However, the dense water formed in this coastal region has lower densities than the dense water formed in the open Adriatic due to lower salinities. Since the coastal area is deeper than the open Adriatic, the observations indicate (i) balanced inward–outward exchange at the deep connecting channels of denser waters coming from the open Adriatic DWF site and less-dense waters coming from the coastal region and (ii) outward flow of less-dense waters dominating in the intermediate and surface layers. The latter phenomenon was confirmed by the model, even if it significantly underestimates the currents and transports in the connecting channels. The median residence time of the coastal area is estimated to be approximately 20 days, indicating that the coastal area may be renewed relatively quickly by the open Adriatic waters. The data that were obtained represent a comprehensive marine dataset that can be used to calibrate atmospheric and oceanic numerical models and point to several interesting phenomena to be investigated in the future.

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted.