A derivative-free optimisation method for global ocean biogeochemical models

The performance of global ocean biogeochemical models, and the Earth System Models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model-data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, inorganic phosphate and inorganic nitrate observations from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers 5 of the 6 parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000 year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a baseline misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

Recent trends in air-sea CO2 fluxes and ocean acidification in the Arabian Sea

<p>Recent observations and modeling evidence indicate that the Arabian Sea (AS) is a net source of carbon to the atmosphere. Yet, the interannual variability modulating the air-sea CO<sub>2</sub> fluxes in the region, as well as their long-term trends, remain poorly known. Furthermore, while the rising atmospheric concentration of CO<sub>2</sub> is causing surface ocean pH to drop globally, little is known about local and regional acidification trends in the AS, a region hosting a major coastal upwelling system naturally prone to relatively low surface pH. Here, we simulate the evolution of air-sea CO<sub>2</sub> fluxes and reconstruct the progression of ocean acidification in the AS from 1982 through 2019 using an eddy-resolving ocean biogeochemical model covering the full Indian Ocean and forced with observation-based winds and heat and freshwater fluxes. Additionally, using a set of sensitivity simulations that vary in terms of atmospheric CO<sub>2</sub> levels and physical forcing we quantify the variability of fluxes associated with both natural and anthropogenic CO<sub>2</sub> and disentangle the contributions of climate variability and that of atmospheric CO<sub>2</sub> concentrations to the long-term trends in air-sea CO<sub>2</sub> fluxes and acidification. Our analysis reveals a strong variability in the air-sea CO<sub>2</sub> fluxes and pH on a multitude of timescales ranging from the intra-seasonal to the decadal. Furthermore, a strong progression of ocean acidification with an important penetration into the thermocline is simulated locally near the upwelling regions. Our analysis also indicates that in addition to the increasing anthropogenic CO<sub>2</sub> concentrations in the atmosphere, recent warming and monsoon wind changes have substantially modulated these trends regionally.</p>

Role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model

Jellyfish are increasingly recognised as important components of the marine ecosystem, yet their specific role is poorly defined compared to that of other zooplankton groups. This paper presents the first global ocean biogeochemical model that includes an explicit representation of jellyfish and uses the model to gain insight into the influence of jellyfish on the plankton community. The Plankton Type Ocean Model (PlankTOM11) model groups organisms into plankton functional types (PFTs). The jellyfish PFT is parameterised here based on our synthesis of observations on jellyfish growth, grazing, respiration and mortality rates as functions of temperature and jellyfish biomass. The distribution of jellyfish is unique compared to that of other PFTs in the model. The jellyfish global biomass of 0.13 PgC is within the observational range and comparable to the biomass of other zooplankton and phytoplankton PFTs. The introduction of jellyfish in the model has a large direct influence on the crustacean macrozooplankton PFT and influences indirectly the rest of the plankton ecosystem through trophic cascades. The zooplankton community in PlankTOM11 is highly sensitive to the jellyfish mortality rate, with jellyfish increasingly dominating the zooplankton community as its mortality diminishes. Overall, the results suggest that jellyfish play an important role in regulating global marine plankton ecosystems across plankton community structure, spatio-temporal dynamics and biomass, which is a role that has been generally neglected so far.

Around one third of current Arctic Ocean primary production sustained by rivers and coastal erosion

Net primary production (NPP) is the foundation of the oceans’ ecosystems and the fisheries they support. In the Arctic Ocean, NPP is controlled by a complex interplay of light and nutrients supplied by upwelling as well as lateral inflows from adjacent oceans and land. But so far, the role of the input from land by rivers and coastal erosion has not been given much attention. Here, by upscaling observations from the six largest rivers and using measured coastal erosion rates, we construct a pan-Arctic, spatio-temporally resolved estimate of the land input of carbon and nutrients to the Arctic Ocean. Using an ocean-biogeochemical model, we estimate that this input fuels 28–51% of the current annual Arctic Ocean NPP. This strong enhancement of NPP is a consequence of efficient recycling of the land-derived nutrients on the vast Arctic shelves. Our results thus suggest that nutrient input from the land is a key process that will affect the future evolution of Arctic Ocean NPP.